FUNCTIONAL MAGNETIC RESONANCE IMAGING FOR …liu.diva-portal.org/smash/get/diva2:214706/FULLTEXT06.pdf · Functional Magnetic Resonance Imaging for Clinical Diagnosis ... P. Lundberg.¨

Linkoping University Medical Dissertations, No. 1121

FUNCTIONAL MAGNETIC RESONANCE

IMAGING FOR CLINICAL DIAGNOSIS

-EXPLORING AND IMPROVING

THE EXAMINATION CHAIN

MATTIAS RAGNEHED

Division of Radiological SciencesDepartment of Medical and Health Sciences

Center for Medical Image Scienceand Visualization (CMIV)

Linkoping University, Sweden, 2009

Functional Magnetic Resonance Imaging for Clinical Diagnosis-Exploring and Improving the Examination Chain

c© 2009 Mattias Ragnehed

Printed by LiU-tryck,Linkoping 2009.ISBN: 978-91-7393-645-3ISSN 0345-0082

ABSTRACT

Functional Magnetic Resonance Imaging (fMRI) is a relatively new imaging tech-nique, first reported in 1992, which enables mapping of brain functions with highspatial resolution. Functionally active areas are distinguished by a small signalincrease mediated by changes in local blood oxygenation in response to neuralactivity. The ability to non-invasively map brain function and the large numberof MRI scanners quickly made the method very popular, and fMRI have had ahuge impact on the study of brain function, both in healthy and diseased subjects.The most common clinical application of fMRI is pre-surgical mapping of brainfunctions in order to optimise surgical interventions.

The clinical fMRI examination procedure can be divided into four integratedparts: (1) patient preparation, (2) image acquisition, (3) image analysis and (4)clinical decision. In this thesis, important aspects of all parts of the fMRI ex-amination procedure are explored with the aim to provide recommendations andmethods for prosperous clinical usage of the technique.

The most important results of the thesis were: (I) administration of low dosesof diazepam to reduce anxiety did not invalidate fMRI mapping results of primarymotor and language areas, (II) the choice of visual stimuli equipment can have se-vere impact on the mapping of visual areas, (III) three-dimensional fMRI imagingsequences did not perform better than two-dimensional imaging sequences, (IV)adaptive spatial filtering can improve the fMRI data analysis, (V) clinical deci-sions should not be based on activation results from a single statistical threshold.

LIST OF PAPERS

This thesis is based on the following papers, which will be referred to by theirRoman numerals. The articles are reprinted with permission from the respectivepublisher.

PAPER I Influence of Diazepam on clinically designed fMRIM. Ragnehed, I. Hakansson, M. Nilsson, P. Lundberg, B. Soderfeldt,M. Engstrom. Journal of Neuropsychiatry and Clinical Neuroscience,2007; 19(2):164-172. doi:10.1176/appi.neuropsych.19.2.164

PAPER II Projection screen or video goggles as stimulus modality in func-tional magnetic resonance imagingM. Engstrom, M. Ragnehed, P. Lundberg. Magnetic Resonance Imag-ing, 2005; 23:695-699. doi:10.1016/j.mri.2005.04.006

PAPER III Visual Grading of 2D and 3D fMRI compared to image based de-scriptive measuresM. Ragnehed, O. Dahlqvist Leinhard, J. Pihlsgard, S. Wirell, H. Sokjer,P. Fagerstam, B. Jiang, O. Smedby, M. Engstrom, P. Lundberg. Sub-mitted Manuscript, 2009

PAPER IV Restricted Canonical Correlation Analysis in Functional MRI -validation and a novel thresholding techniqueM. Ragnehed, M. Engstrom, H. Knutsson, B. Soderfeldt, P. Lund-berg. Journal of Magnetic Resonance Imaging, 2009; 29(1):146-154.doi:10.1002/jmri.21494

PAPER V Brain lateralisation assessed by fMRI and dichotic listeningH.M. Van Ettinger-Veenstra, M. Ragnehed, M. Hallgren, T. Karlsson,A-M. Landtblom, P. Lundberg, M. Engstrom. Manuscript

http://dx.doi.org/10.1176/appi.neuropsych.19.2.164

http://dx.doi.org/10.1016/j.mri.2005.04.006

http://dx.doi.org/10.1002/jmri.21494

List of Papers vi

Contributions to Papers

PAPER I In this project I was responsible for the study design and the fMRIdata acquisition. I also performed the fMRI data analysis and was themain author of the manuscript.

PAPER II I was involved in the planning and set-up of the study, participated inthe design of the visual stimuli and did some of the analysis of thedata. I also made important contributions to the interpretation of theresults.

PAPER III I performed all fMRI data analysis and I was responsible for designand performance of the Visual Grading. In addition I performed allstatistical evaluations and wrote the manuscript.

PAPER IV In this project I was responsible for data collection and all data anal-ysis. I came up with the idea for and developed the significance esti-mation method and wrote the manuscript.

PAPER V I was involved in the planning and design of the study and came upwith the idea to include dichotic listening. I did some of the fMRIanalysis and wrote part of the manuscript.

Other peer reviewed publications not included in the thesisRegular articles

• Paradigm design of sensory-motor and language tests in clinical fMRI.M. Engstrom, M. Ragnehed, P. Lundberg, B. Soderfeldt. Clinical Neuro-physiology, 2004; 34(6):267-277

Abstracts

• Influence of performance-related language ability on cortical activa-tion.H.M. Veenstra, J. Pettersson, C. Nelli, M. Ragnehed, A. McAllister, P.Lundberg, M. Engstrom. Human Brain Mapping, Honolulu, 2009.

• Brain lateralization assessed by fMRI and dichotic listening.H.M. Veenstra, M. Ragnehed, M. Hallgren, P. Lundberg, M. Engstrom. Hu-man Brain Mapping, Honolulu, 2009.

vii List of Papers

• Using Visual Grading Characteristics for the evaluation of differentfMRI data acquisition methodsM. Ragnehed, O. Dahlqvist Leinhard, O. Smedby, M. Engstrom, P. Lund-berg. European Society for Magnetic Resonance in Medicine and Biology,Valencia, 2008.

• Does diazepam influence the BOLD response? M. Ragnehed, M. En-gstrom, P. Lundberg. International Society for Magnetic Resonance inMedicine, Berlin, 2007.

• Influence of diazepam on clinically-designed fMRI. B. Soderfeldt, M.Ragnehed, I. Hakansson, P. Lundberg, M. Nilsson, J. Ahlner, M. Engstrom.Journal of Neuropsychiatry and Clinical Neuroscience, 18, 2006.

• LI and the effect of thresholding. M. Ragnehed, M. Engstrom, B. Soder-feldt. European Society for Magnetic Resonance in Medicine and Biology,Copenhagen, 2004.

• Comparison between fMRI and Wada test. L. Borjesson, J. Stockhaus,H. Gauffin, M. Ragnehed, P. Lundberg, B. Soderfeldt. EPILEPSIA, 45(S3),2004.

• Quantitation of atherosclerosis in a minipig model with MRI and dy-namic contours. O. Smedby, M. Ragnehed, A. Knutsson, L. Jacobsson,X. Yuan, R. Andersson. Society of Cardiovascular Magnetic Resonance,Barcelona, 2004.

• Localization of Signed and Heard Episodic and Semantic MemoryTasks using fMRI. P. Nystrom, M. Ragnehed, O. Friman, M. Engstrom,P. Lundberg, H. Knutsson, B. Soderfeldt. The 9th International Conferenceon Functional Mapping of the Human Brain, June 19-22, 2003, New York,NY. Neuroimage, 19(2).

• Comparing CCA and SPM99. M. Ragnehed, O. Friman, P. Lundberg, B.Soderfeldt, H. Knutsson. The 9th International Conference on FunctionalMapping of the Human Brain, June 19-22, 2003, New York, NY. Neuroim-age, 19(2).

LIST OF ABBREVIATIONS

2D Two-Dimensional

3D Three-Dimensional

#voxels number of activated voxels

ADP Adenosine Di-Phosphate

ANOVA Analysis of Variance

ATP Adenosine Tri-Phosphate

auROC area under ROC curve

BOLD Blood Oxygenation Level Dependent

CBF Cerebral Blood Flow

CBV Cerebral Blood Volume

CCA Canonical Correlation Analysis

CMR Cerebral Metabolic Rate

CMRGlc Cerebral Metabolic Rate of Glucose

CMRO2 Cerebral Metabolic Rate of Oxygen

CT Computed Tomography

EEG ElectroEncephaloGraphy

Abbreviations x

EPSP Excitatory Post-Synaptic Potential

FWHM Full Width at Half Maximum

GABA Gamma-AminoButyric Acid

GLM General Linear Model

hr Haemodynamic Response

hrf Haemodynamic Response Function

IPSP Inhibitory Post-Synaptic Potential

LI Lateralisation Index

MEG MagnetoEncephaloGraphy

MRI Magnetic Resonance Imaging

mROC modified ROC

PET Positron Emission Tomography

PRESTO PRinciples of Echo-Shifting with a Train of Observations

ROC Receiver Operating Characteristic

SENSE SENSitivity Encoding

SNR Signal to Noise Ratio

SPECT Single Photon Emission Computed Tomography

T Tesla

TCA Tri-Carboxylic Acid

TMS Transcranial Magnetic Stimulation

TR Repetition Time

tSNR temporal Signal to Noise Ratio

VG Visual Grading

VGC Visual Grading Characteristic

TABLE OF CONTENTS

Abstract iii

List of Papers v

Abbreviations x

I Introduction 1

CHAPTER 1 Introduction 31.1. Medical Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2. fMRI History . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

CHAPTER 2 Brain Activity 72.1. Neuronal Activity . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2. Neuronal Energy Requirements . . . . . . . . . . . . . . . . . . . 92.3. Physiological Effects of Neuronal Activity . . . . . . . . . . . . . 10

CHAPTER 3 Functional MRI 133.1. Detecting Brain Activity . . . . . . . . . . . . . . . . . . . . . . 13

3.1.1. Paradigm Design . . . . . . . . . . . . . . . . . . . . . . 133.1.2. Pre-processing . . . . . . . . . . . . . . . . . . . . . . . 143.1.3. The BOLD Response . . . . . . . . . . . . . . . . . . . . 153.1.4. Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Table of Contents xii

CHAPTER 4 Clinical Applications of Functional MRI 194.1. Special Requirements . . . . . . . . . . . . . . . . . . . . . . . . 204.2. Tumour Resection . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2.1. Task Selection . . . . . . . . . . . . . . . . . . . . . . . 214.3. Surgical Treatment of Epilepsy . . . . . . . . . . . . . . . . . . . 22

4.3.1. Source Localisation . . . . . . . . . . . . . . . . . . . . . 234.4. Brain Plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

CHAPTER 5 Aims 25

II Methods, Results and Discussions 27

CHAPTER 6 Patient Preparation 316.1. Anxiolytics and fMRI . . . . . . . . . . . . . . . . . . . . . . . . 31

6.1.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . 316.1.2. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 326.1.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 336.1.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 33

CHAPTER 7 Data Acquisition 357.1. Visual Stimulus Delivery . . . . . . . . . . . . . . . . . . . . . . 35


7.2. Imaging Sequences . . . . . . . . . . . . . . . . . . . . . . . . . 377.2.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . 387.2.2. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 397.2.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 397.2.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 39

CHAPTER 8 Analysis 418.1. Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 41


CHAPTER 9 Interpretation of fMRI Results 459.1. Objective and Subjective Measures . . . . . . . . . . . . . . . . . 45

xiii


9.2. Significance Testing of RCCA Results . . . . . . . . . . . . . . . 489.2.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . 489.2.2. Methods and Results . . . . . . . . . . . . . . . . . . . . 489.2.3. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 51

9.3. Thresholding Issues for Clinical fMRI . . . . . . . . . . . . . . . 529.3.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . 529.3.2. Lateralisation of Brain Function . . . . . . . . . . . . . . 529.3.3. Subjective Threshold Selection . . . . . . . . . . . . . . . 539.3.4. Presurgical fMRI . . . . . . . . . . . . . . . . . . . . . . 549.3.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 55

CHAPTER 10 Summary 57

Bibliography 59

Acknowledgements 73

III Papers 75

Part I

Introduction

1INTRODUCTION

This thesis is divided into three different parts. In Part I, Introduction, materialnecessary to understand the concept of fMRI is presented. Then some clinicalapplications of fMRI that highlights the importance of proper examination proce-dures are introduced. Finally the specific aims of this thesis are listed. In Part II,Methods, Results and Discussions, the research conducted to answer the specificresearch questions raised in Chapter 5, Aims, is reviewed, followed by a discus-sion of the achieved results. In Part III, Papers, the articles that provide the basisof the thesis are reprinted.

1.1 Medical ImagingMedical imaging, including technologies such as microscopy, ultrasound, X-ray,Computed Tomography (CT), Magnetic Resonance Imaging (MRI) etc., have be-come an immensely important tool in many medical disciplines. The examinationchain for medical imaging involves several important procedures,

1. patient preparation

2. image acquisition

3. image analysis

4. visualisation and diagnosis

All procedures of the examination chain have to be performed in a professionaland standardised manner in order to provide proper information to the clinicianwho is responsible for making the diagnose.

Chapter 1. Introduction 4

fMRI is a perfect example to demonstrate the importance of all proceduresinvolved in the medical imaging examination chain:

1. the patient (or test subject) must be prepared for the examination and receiveproper instructions on how to perform the behavioural task presented duringscanning. If the task is not executed as intended the result of the examinationwill be misleading, other areas then expected might be highlighted and/orsome desired functional areas will not be detected at all

2. it is important that the selected data collection sequence has good functionalcontrast, limited signal drifts and provide enough tissue contrast to allowproper registration

3. robust and efficient statistical data analysis methods are required to extractthe functional information in the quite noisy fMRI data

4. the statistical results need to be properly presented to ensure correct inter-pretation of the results

If these steps are performed in a standardised and professional manner, fMRI canbe a useful clinical tool. This chain of events will be referred to throughout thethesis, especially Part II, Methods, Results and Discussions will closely follow theflow of the clinical imaging examination chain.

1.2 fMRI HistoryIn 1990 Ogawa et al. [1990] performed some MRI experiments on rats at highfield strengths. By manipulating the blood oxygenation level they found thatde-oxygenated blood caused distortions on gradient-echo images. They specu-lated that this effect, which later was termed Blood Oxygenation Level Dependent(BOLD) contrast, would make it possible to measure changes in brain activity.

In 1992 the first reports of functional mapping using the BOLD contrast werereported. Using long blocks of sustained visual stimulation followed by rest,Kwong et al. [1992] reported a sharp increase of the MRI signal in relevant brainregions that remained for the whole stimulation period. The result was replicatedin a study published shortly afterwards by Ogawa et al. [1992]. Bandettini et al.[1992] used a motor task to induce brain activation and obtained similar results.Later that year, Blamire and colleagues reported that even short duration stimuligave rise to the same kind of MR signal increase [Blamire et al., 1992]. How-ever, they also noted that there was a small delay, of approximately 3.5 seconds,between stimulus onset and the observable signal increase.

5 1.2 fMRI History

Shortly after the introduction of BOLD fMRI in 1992 [Bandettini et al., 1992;Blamire et al., 1992; Kwong et al., 1992; Ogawa et al., 1992], several researchgroups repeated and extended their experiments. Ever since, fMRI has been usedto answer research question about the functional organisation of the human brain.Nowadays, MRI scanners capable of performing functional imaging are availablein most hospitals. The high availability of MR scanners has resulted in an ex-plosion of fMRI studies, see Figure 1.1, and fMRI is now the standard tool forfunctional neuroimaging. As the fMRI technique has matured and been refinedthe acceptance to use fMRI for clinical purposes is increasing. The most commonclinical application of fMRI is perhaps pre-surgical mapping of eloquent areasand evaluation of hemispheric language dominance prior to temporal lobectomyof certain epilepsy patients.

1994 1996 1998 2000 2002 2004 2006 20080

400

800

1200

1600

2000

YEAR

#P

UB

LIC

AT

ION

S

fMRI PUBLICATIONS IN PUBMED

FIGURE 1.1. Publications on fMRI by year. The number of fMRI publications inmajor scientific journals is has increased rapidly since its inception in 1992.

2BRAIN ACTIVITY

Understanding the functional organisation of the intriguing human brain has beenthe ultimate goal for scientists for a long time. A multitude of methods have beenused in the search for knowledge about the inner functioning of the brain. Im-portant information has been obtained by studying functional deficits caused bybrain lesions. More recently, the electric and magnetic signals originating fromneural activity have been studied using Electroencephalography (EEG) and Mag-netoencephalography (MEG). Since the electrodes have to be placed outside of theskull, the ability to identify the location of the neuronal signals is limited. Usingtechniques like positron emission tomography (PET) and single photon emissioncomputed tomography (SPECT), it became possible to acquire images of brainfunction. The main drawback of these imaging techniques is that they rely on theuse of radioactive tracers, which has limited their use. With the advent of fMRI,a completely non-invasive alternative for functional imaging became available. Inthis chapter the principles of BOLD fMRI will be reviewed. Most of the materialhas been extracted from the textbooks by Aguirre and D’Esposito [2001],Brodal[2003], Buxton [2002], Huettel et al. [2004], and Jezzard et al. [2001].

2.1 Neuronal ActivityThe human brain contains about 1011 neurons. About 20% of the neurons are lo-cated in the cerebral cortex. Just like any other cell, the neuron has a cell bodycontaining cytoplasm, organelles and a nucleus. A typical neuron, see Figure 2.1,has branches, called dendrites, forming the dendritic tree, which is the main in-formation receiving network of the neuron. The number of dendrites and the sizeand shape of the dendritic tree shows great variability. The neurons also have oneaxon. The axon is used to transmit information to other neurons through connec-

Chapter 2. Brain Activity 8

tions, called synapses, located on the dendritic tree or directly on the cell bodyitself. Even though each neuron only has one single axon, the neuron can com-municate with several other neurons due to the fact that the axon usually undergoextensive branching. It has been estimated that there is about 1014 synapses inthe human brain, which means that on average each neuron has a few thousandsynapses [Drachman, 2005].

FIGURE 2.1. A detailed view of a typical neuron with a dendritic tree and abranching myelinated axon. [Picture from Wikipedia Commons.]

The chemical environment in the brain is maintained at a state which is faraway from chemical equilibrium. For instance, inside the neuron the concentra-tion of K+ is larger than on the outside, and on the outer side of the neuronalmembrane there is a greater concentration of Na+, Ca2+ and Cl− than on the in-side. Thus there is an electrical potential between the inside and outside of the cellmembrane (negative inside). This resting membrane potential is about −40 mV.Since the neuronal membrane prevents unrestricted diffusion of ions along theirconcentration gradients, they are unable to reach chemical equilibrium. However,the membrane contains selective ion channels where specific ions are allowed topass. These ion channels have gating mechanisms, which open or close for iontransport, controlled by the action of specific molecules. The gates may also be

9 2.2 Neuronal Energy Requirements

voltage dependent, opening when the electrical potential across the membranereaches a threshold value. Apart from passive diffusion along concentration gra-dients through ion channels, there are mechanisms for transporting ions againstthe concentration gradients. These so called ion pumps require energy, which isreleased by adenosine triphosphate (ATP), for their action.

The activity of neurons may be divided into integrative or signalling activity.Integrative activity refers to the collection and integration of inputs from manysynapses on the dendritic tree, while signalling activity refers to transmission ofthe result of the integration of inputs to other neurons. The exchange of informa-tion at the synapses is mediated by different neurotransmitters released by the pre-synaptic process of the axon. The neurotransmitters influence the environment atthe post-synaptic membrane inducing either an excitatory post-synaptic potential(EPSP) or an inhibitory post-synaptic potential (IPSP). The most common neu-rotransmitters are glutamate and gamma-Aminobutyric acid (GABA). Glutamateopens normally closed Na+ channels resulting in an influx of Na+ into the cellthereby reducing the electrical potential across the membrane. The local depolar-isation of the post-synaptic membrane is an example of an EPSP. Thus, glutamateis an excitatory neurotransmitter [Meldrum, 2000]. GABA on the other hand en-ables an active influx of Cl− and efflux of K+, thus increasing the membranepotential, a hyperpolarisation. This is known as an IPSP and accordingly, GABAis known as an inhibitory neurotransmitter [Li and Xu, 2008].

The neuron integrates the effects of the EPSPs and IPSPs from all of itssynapses. If the net effect, over a brief time interval, is to depolarise the mem-brane below a threshold voltage a large number of voltage gated sodium channelswill open. There will be a large influx of Na+ into the cell and it will be fur-ther depolarised and even more ion channels will open. This depolarisation wavewill propagate down the axon and it is known as an action potential or ’nerve im-pulse’. When the action potential reaches the pre-synaptic axon terminal a num-ber of events occur that leads to the release of neurotransmitters into the synapticcleft. The transmitter interacts with receptors that control ion channels on thepost-synaptic membrane initiating either an EPSP or IPSP.

2.2 Neuronal Energy RequirementsThe human brain has no internal energy stores and thus requires a continuous sup-ply of energy and oxygen. Constituting a mere 2–3% of the body mass, the brainstill receives about 15% of the total cardiac output of blood [Siegel et al., 1999].The distribution of blood within the brain is heterogeneous, gray matter receivesseveral times more blood per gram of tissue than white matter. The amount ofblood delivered per gram of tissue to gray matter is comparable to that in heart tis-

Chapter 2. Brain Activity 10

sue. When a group of neurons become active their metabolic demands increase,which results in regional changes in cerebral blood flow.

The production of IPSPs, EPSPs and action potentials does not consume anyenergy. They simply use the fact that the system is in a non equilibrium state, andtheir actions moves the system closer to equilibrium. However, energy is requiredin order to restore the resting potential and concentration gradients of the differentions after neural activity. To regenerate the resting potential and ion concentrationgradients so called ion pumps are utilised. Since these pumps act to restore a nonequilibrium state they require a source of energy to function.

The primary source of energy in the human brain is ATP. Conversion of ATPto adenosine diphosphate (ADP) releases a large amount of energy. In order toutilise the energy stored in the ATP/ADP system the conversion of ATP to ADPis directly coupled to energy consuming processes. For instance, the Na/K-pump,which moves three Na+ ions out of the cell and two K+ into the cell, consumesone ATP molecule. The Na/K-pump is important for the recovery of the neuronfollowing an action potential and for maintaining the resting potential. At thesynapse there are a number of other mechanisms involved in the recovery after anaction potential. For instance, the neurotransmitters have to be brought back intothe pre-synaptic terminal. This process is governed by mechanisms that consumesATP. In summary, the neural activity itself does not require any energy but therestoration of the chemical gradients and the resting potential does.

After ATP has been consumed, the ADP must be converted back into ATP.This is mainly done through glycolysis, in which the consumption of one glucosemolecule results in two ATP and a rest product called pyruvate. The pyruvate iseither further reduced into lactate (anaerobic glycolysis) or, if oxygen is present,the pyruvate enters a complex chain of reactions called aerobic glycolysis, wherean additional 34 ATP is produced. Anaerobic metabolism, generating only twoATP per consumed glucose molecule, is very inefficient when compared to aero-bic metabolism, generating 36 additional ATP. However, anaerobic metabolism isabout 100 times faster than aerobic metabolism.

2.3 Physiological Effects of Neuronal ActivityAs early as 1890 the influential psychologist William James wrote ([James, 1890],as quoted by Buxton [2002]):

We must suppose a very delicate adjustment whereby the circulationfollows the needs of the cerebral activity. Blood very likely may rushto each region of the cortex according as it is most active, but of thiswe know nothing.

11 2.3 Physiological Effects of Neuronal Activity

With the introduction of PET it was possible to test James’s assumption onhumans. Using PET one can measure cerebral blood volume (CBV), cerebralblood flow (CBF), cerebral metabolic rate of glucose consumption (CMRGlc),and cerebral metabolic rate of oxygen consumption (CMRO2). Several studieson animals and humans have established that there is a close relationship be-tween local functional activity and local glucose metabolism. Activation studieshave revealed increased CMRGlc in activated brain regions. In addition, CMR-Glc shows a response which correlates with different levels of functional activity[Schwartz et al., 1979; Sokoloff, 1977; Kadekaro et al., 1985]. A PET study witha somatosensory task showed a task induced increase of CBF of 26% and CMR-Glc increase of 17% [Ginsberg et al., 1988]. In another study of the sensorimotorcortex with PET an 50% increase of both CBF and CMRGlc was observed duringactivity [Fox et al., 1988].

While glucose consumption and blood flow increases substantially and by ap-proximately the same amount during activation the oxygen consumption rate doesnot. Large discrepancies between CBF and CMRO2 changes have been observed,while CBF increases 25–50% CMRO2 increases only a few percent [Fox et al.,1988]. Using PET, the ratio of percent change in CBF to percent change inCMRO2 have been found to be 3–6 [Fox and Raichle, 1986]. This imbalanceresults in a substantial drop in deoxy-haemoglobin in venous blood.

In summary, during brain activation there is a large increase in CBF and CMR-Glc localised to the activated brain region. The large increase of blood flow makesPET and fMRI possible. It is also important to realise that the blood flow changealso depend on the intensity of the stimuli such that the flow changes reflects theamount of neural activity. The increase in oxygen consumption is, however, muchsmaller than the flow increase during brain activity. This leads to a lower oxygenextraction fraction during activity and thus an increased deoxy-haemoglobin con-tent in venous blood. The drop in deoxy-haemoglobin content forms the basis ofthe BOLD response used in most fMRI studies, see Chapter 3, Functional MRI.

3FUNCTIONAL MRI

3.1 Detecting Brain ActivityWhen performing ordinary brain imaging, one image is often sufficient to classifydifferent tissue types. The fundamental difference with functional brain imagingis that one is interested in evaluating signal changes over time, thus images arecollected for an extended time interval, say 5–10 minutes. During the fMRI ex-periment image volumes are continuously collected with a repetition time (TR) of2–4 seconds, resulting in a total of 200–300 images for the whole time-series.During the image acquisition, the subject performs a specific task interleavedwith a control task (rest). There are two main types of task sequence designs(paradigms), event-related designs [Dale and Buckner, 1997] and block designs.In event-related designs the subject is confronted with short duration (usually <1 second) stimuli interleaved with a base-line condition of longer duration (usu-ally 5–30 seconds). In blocked designs the neural activity is sustained for longerperiods, usually 15–30 seconds, inter-spaced with periods of baseline activity.

To identify voxels activated by the task the collected voxel time-series arecompared to a model of the expected BOLD signal of an activated voxel. Voxelswhose time-series resemble the model close enough are then labelled as active,which results in a map of brain structures activated by the behavioural paradigmused.

3.1.1 Paradigm DesignWhen designing a fMRI paradigm it is most important to determine exactly whichfunction(s) to map. Then a task which involves the desired function(s) and a con-trol task that. Ideally, the control task should elicit the same cognitive processes

Chapter 3. Functional MRI 14

as the activation task except for the function(s) to map. Designed this way theexperiment, relying on the concept of pure insertion [Price and Friston, 1997;Zarahn et al., 1997a], will highlight the desired function(s).

Early fMRI studies did not use any explicit base-line task, instead the subjectwas simply told to relax between the task periods. It has been shown that duringpassive rest significant cognitive processes are present. For instance the brain maybe engaged in planning and problem solving, episodic memory encoding and freethoughts. These are all processes which one cannot control. It is therefore veryimportant to design a control task that keeps the subject in a desired cognitive state[Binder et al., 1999; Stark and Squire, 2001; Luca et al., 2006].

As stated earlier there are two main categories of paradigms, blocked de-signs and event-related designs, which have different strengths and weaknesses.Blocked designs have high detection power, are quite insensitive to small varia-tions of the individual BOLD response, and are conceptually easy to understand.Event-related designs are appropriate for estimation of the BOLD response, tofind timing differences between activated regions and they are more flexible thanblocked designs [Aguirre and D’Esposito, 2001; Huettel et al., 2004]. In mostclinical applications high detection power is desirable, thus blocked designs arefar more common than event-related paradigms in clinical settings.

If possible, behavioural data should always be recorded. Otherwise it is im-possible to know for sure that the subject performed the task as instructed and noconclusions can be drawn from the results of the experiment. It is crucial that thetask instructions are very clear so that the subject performs the task correctly. It isalso beneficial to train the subject using a realistic training version of the paradigmprior to the functional scanning session to reduce the risk of faulty execution ofthe task [Stippich, 2007].

3.1.2 Pre-processingBefore the statistical analysis of the data is performed a number of pre-processingsteps needs to be done. Even when different devices such as bite bars, vacuumcushions or rubber bands, are used to restrict involuntary head movements, headmotion during image acquisition is inevitable. Thus one has to re-orient the im-ages to make sure that any voxel contain the same brain tissue in all image volumes[Friston et al., 1996; Ardekani et al., 2001; Morgan et al., 2007].

In addition, the image slices within a volume are usually collected at differenttimes. This means that the BOLD response to a stimuli is sampled at differenttimes for different slices. This will reduce the possibility to detect activated vox-els, especially for long TR, event-related designs. Thus, in some cases it is impor-tant to apply a temporal interpolation such that all data appear to be sampled atthe same time [Aguirre et al., 1998].

15 3.1 Detecting Brain Activity

It is also common practice to smooth, i.e.spatially low-pass filter, the imageswith a Gaussian kernel of pre-specified size before the statistical analysis. TheGaussian filter is often recommended to be about 2.5 × voxel-size full width athalf maximum (FWHM). Low-pass filtering is utilised to improve the signal tonoise ratio (SNR) in the images. Since the images are contaminated with randomnoise, a weighted average of neighbouring voxels will reduce the noise compo-nent. Smoothing, however, reduces the spatial resolution and after smoothing finescale details of the images are lost. The optimal size of the smoothing filter isdependent on image data properties as well as the specific aims of the study [Tri-antafyllou et al., 2006; Weibull et al., 2008b]. Hence, it is important to adjust theFWHM of the smoothing filter to match the requirements of the specific study.

In addition, the voxel time-courses are usually contaminated by low-frequencynoise [Zarahn et al., 1997b]. Such signal drifts during the experiment can becaused by scanner instability, head motion and breathing. If left unmodelled thelow frequency noise will reduce the detection power of activated voxels. Thus it isimportant to remove the signal drifts, for example by an ordinary high-pass filter.

3.1.3 The BOLD ResponseBrain activity causes a slight change of the MR signal known as the haemody-namic response (hr). The hr depends, apart from physiological factors, primarilyon the duration of the stimuli. The basic shape of the hr is the same over subjectsand brain regions, making it is possible to generate models of the hr. Several ac-curate haemodynamic response function (hrf) have been suggested [Friston et al.,1995; Buxton et al., 2004]. Since the hr is (nearly) linear [Boynton et al., 1996;Dale and Buckner, 1997], the hrf can be used to accurately model the predictedBOLD response to any task given a reasonable model of the task induced neu-ral activity. The predicted BOLD response is given by convolving the hrf with themodel of the neural activity. However, it is important to note that there are regionalas well as individual differences in the shape of the BOLD response [Aguirre et al.,1998]. To capture these differences in the BOLD response, it is common to use amodel that consist of a set of basis function that include derivatives of the canon-ical hrf. By forming linear combinations of the basis functions, subtle changes inshape and latency of the hr are captured. In Figure 3.1 signal changes after shortduration sequential finger tapping is shown. In the left panel several individualresponses are shown and in the right panel the mean of those responses is shown.The mean response closely matches the shape of the hrf shown in the left panel ofFigure 3.2.

The large variability of the hr is due to the presence of noise and the factthat the amplitude of the BOLD signal change is quite small, 0.5–3% for 1.5T MR scanners. This is about the same order of magnitude as the noise in the


FIGURE 3.1. Measured haemodynamic responses (hr) in the motor cortex afterfrom a finger tapping task. Left panel shows the individual haemodynamicresponse (hr) to several separate stimuli of an activated area. Right panelshows the mean of those responses.

images. Thus, in order to correctly identify BOLD signal changes that are due toneural activity one has to repeat the stimuli several times to allow averaging of theresponse.

As shown in Figure 3.1, the first measurable changes in the BOLD signal oc-curs after about 3.5 seconds, it then rises to its peak value at about six secondsafter the onset of brief neural activity. For long duration activity, see Figure 3.2,the peak extends into a plateau. After reaching its peak, the BOLD signal de-creases to below baseline and remain there for several seconds before returning to

FIGURE 3.2. Left panel shows the modelled response to a short duration stimuli,indicated by a spike at 0 s. Right panel shows the modelled response after along duration activity indicated by a boxcar starting at 0 s.

17 3.1 Detecting Brain Activity

FIGURE 3.3. An example of an experimental block paradigm and the BOLDmodel constructed by convolution of the paradigm and the haemodynamicresponse function (hrf).

baseline.

3.1.4 AnalysisTo detect voxels activated by the task, a model of the expected signal changes in-duced by the paradigm is compared statistically with the collected BOLD signaltime-series using correlation or linear regression. This analysis is usually per-formed within the General Linear Mode (GLM) framework [Friston et al., 1995;Worsley and Friston, 1995]. Activations are assessed by calculating the statisticalsignificance of the coefficients, most often using a t-test or F-test of the correlationcoefficients or linear regression coefficients. The signal model is constructed byconvolution of the paradigm time-course with the hrf, see Figure 3.3.

The GLM is formulated as

Y = Xβ + ε, (3.1)

where Y is a matrix of observed voxel time-series,X is a matrix of basis functions,ε is an error term with variance σ2 and β a vector of parameters to be estimated.The least squares solution to Eq. (3.1) can be written as

β =(XTX

)−1XTY. (3.2)

The estimated parameters β describe the amplitude of the task induced BOLDresponse. If β 6= 0 then there is a task induced effect in the acquired images.


However, since the data are contaminated with noise, there is always the possibil-ity that the β become non-zero by chance alone. To control the effect of randomnoise, the significance of the estimated parameters can be tested by calculating atest statistic T and then compare the T -values with a known distribution. To testif the linear combination cT β 6= 0, the T -statistic of the linear combination cT β isgiven by

T =cT β√

σ2cT (XTX)−1c), (3.3)

which is compared to a Students t-distribution. If the statistic value exceeds the99th percentile, the effect is said to be statistically significant at p < 0.01. Thestatistical analysis results in an image of the statistical value for each voxel. An ac-tivation map is created by considering all voxels where the statistic value exceedsa specified threshold value, for instance the T -value corresponding to p < 0.001,to be active. The significance level is chosen to ensure that the number of falsepositives is kept low enough. If p < 0.001 and an image volume of 64× 64× 30voxels is analysed, more than 100 voxels being active due to chance alone is ex-pected. The resulting activation map is usually colour coded and overlaid on ananatomical reference image, see Figure 3.4.

FIGURE 3.4. An example of fMRI results overlaid on an anatomical referenceimage. Data is from a left hand finger tapping task. The subject has atumour on the right hand side of the brain.

4CLINICAL APPLICATIONS OF

FUNCTIONAL MRI

fMRI has had a huge impact on understanding normal brain function. How-ever, its use in clinical practice has been relatively limited. One important rea-son is that the fMRI procedure, including patient information, scanning param-eters, paradigm design and data analysis procedures, are not sufficiently stan-dardised. Thus recommendations and improvements to all parts of the imagingchain presented in Chapter 1, Introduction, are needed. Despite these limitations,applications are emerging in both clinical practice and in clinical neurosciences[Matthews et al., 2006]. Some clinical application of fMRI will be described inthis chapter, namely:

I Localisation of normal brain function in relation to brain lesions, see Sec-tions 4.2 and 4.3.

I Demonstration of abnormal brain function, see Section 4.3.1.

I Elucidation of functional development and recovery mechanism, see Sec-tion 4.4.

Some of the problems encountered in these applications are the main motive forthe research aims to be addressed in this thesis, see Chapter 5, Aims.

A fundamental question when fMRI is used in the clinic is whether the activa-tion pattern obtained in a single session truly represents the functional anatomy ofthe subject. Several studies have focused on the issue of reproducibility of fMRIresults. In general, it has been shown that there is good inter-session reproducibil-ity [Otzenberger et al., 2005; Smith et al., 2005]. However, it is important toascertain that the activation tasks are well designed and that the task is restrictive

Chapter 4. Clinical Applications of Functional MRI 20

enough to force the subject to perform the task in a consistent way. In addition,during the functional task, it is important to monitor behaviour to make sure thatthe subject is really performing the task as expected. An additional difficulty isthe fact that patients often suffer from impaired functions, which may render itdifficult to perform certain tasks. Therefore it is most important to carefully adaptthe tasks to match the abilities of the patient [Sunaert, 2006; Stippich, 2007].

4.1 Special RequirementsIn general, a fMRI examination of a patient require more resources than exam-inations of healthy volunteers. Many fMRI patients have neurological deficitsthat, depending on the location of the lesion, influence their behaviour in differentways. Most prevalent among the fMRI patients are disturbed motor functions, lan-guage and memory deficits and reduced mental capacity. These conditions makeit more difficult to perform successful functional imaging, mainly because the pa-tients find it difficult to perform the cognitive or sensory-motor task. However, inmost cases it is still possible to obtain satisfactory results if sufficient training isperformed with patient prior to the fMRI examination.

In many cases it is important not only to practice the task beforehand, but alsoto adapt the task to the individual patient. In case of partial paresis, the patientmay not be able to perform some movements, it is then necessary to adapt themotor task to match the ability of the the individual patient. If complete paresis ofthe targeted limb is present, knowledge about the functional organisation may beachieved by manually moving the affected limb. Some information may also begained by examining the contralateral limb [Stippich, 2007].

4.2 Tumour ResectionLocalisation of eloquent brain functions prior to tumour resection is the most wellestablished clinical application of fMRI [Sunaert, 2006; Stippich, 2007]. The ul-timate goal of surgical treatment of brain tumours is to completely remove thepathological tissue, while minimising damage to healthy tissue in order to reducethe risk of inducing new (possibly permanent) neurological deficits. It is thereforeimportant that the resection margin does not extend into functionally intact areas.

Mapping of eloquent functional areas has traditionally been performed usinginvasive methods, for instance using intra-operative cortical stimulation (patientawake) or extra-operative mapping by implantation of a subdural grid. Thesemethods are accurate, but there are some important disadvantages. They requireextra surgery time, or even a separate surgical procedure for the mapping of brain

21 4.2 Tumour Resection

function. In addition, a larger craniotomy than required for the tumour removalmay be needed for the mapping. As both morbidity and cost of surgery is relatedto the time required for the procedure, there is clear benefit both for the patientand the clinic if prior knowledge about functional organisation is available.

FMRI allows non-invasive mapping of eloquent areas to be performed priorto surgery. To date, functional mapping of motor, sensory and language functionsare considered to be appropriate for pre-surgical mapping [Bookheimer, 2007;Stippich, 2007; Berntsen et al., 2008; Pujol et al., 2008]. By combining fMRIresults with anatomical reference images, the relationship between lesion mar-gin and functional areas can be established. This information can be used forpre-operative risk assessment, allowing the patient and physician to make an in-formed decision about treatment options. If functionally intact tissue is too closeto the desired resection margin, the fMRI results indicate that intra-operative map-ping is required. As the distance between resection margin and functional activityincreases, the risk for postoperative neurologic deficits decreases [Yetkin et al.,1998b; Haberg et al., 2004].

Some groups have proposed that there is a safe distance between resectionmargin and eloquent cortex, where surgery can be considered safe [Yetkin et al.,1998b; Haberg et al., 2004]. However, defining such a safe distance between ac-tivity and resection margins is problematic for several reasons. For instance, thedistance will be influenced by several parameters including the imaging sequenceused, analysis options (smoothing) and the statistical threshold used to define ac-tivations. In addition, the safe distance is influenced by the local anatomy; it islikely that a larger distance is needed along the cortex, while a shorter distancemay be considered safe if moving across a sulcus.

In conclusion, fMRI can contribute to surgical planning in three ways:

I Risk assessment of post-surgical neurologic deficits

I Selecting patients for intra-operative mapping

I Planning and guiding the neurosurgical procedure

4.2.1 Task SelectionWhen using fMRI for pre-surgical mapping, it is natural to choose the activationtask on the basis of lesion location and behavioural symptoms. However, sincethere is variability between anatomy and function even in the healthy brain, itmay be beneficial to map the anatomy of various functions [Hirsch et al., 2000].In addition, the normal relationships between function and anatomy may be dis-turbed by mass lesions and functional areas may also be relocated in response topathology.


Sensory-motor functions have been mapped using a variety of different taskssuch as finger tapping, hand clenching, tongue movement, lip movement, footmovement and toe movement. Sensory functions have been examined by rub-bing, stroking or brushing the relevant body part. For mapping of Broca’s area,different forms of word generation tasks have been utilised. Since speaking outloud induces extensive head motion, the patients are in most cases instructed toperform silent word generation. Even so, involuntary tongue movements may bea problem. The word generation tasks include verb generation tasks, picture nam-ing tasks, verbal fluency task or recital tasks [Engstrom et al., 2005]. Activity inWernicke’s area has been induced by tasks that require language comprehensionsuch as semantic or grammatical judgment tasks. Another way of activating Wer-nicke’s area is to let the patient listen to spoken language or read written language.Mapping of visual areas has been performed by having the patient view flickeringcheckerboards, flashing lights or light emitting diodes.

4.3 Surgical Treatment of EpilepsyEspecially for patients suffering from pharmaco-resistant temporal lobe epilepsy,surgical treatment offers the possibility of improved seizure control or even cure.However, surgical planning requires an understanding of the language lateralisa-tion (hemispheric dominance) of the individual patient. The standard method forevaluation of language laterality is the Wada procedure [Wada and Rasmussen,1958]. In this procedure, an anaesthetic (barbiturate) is delivered to into one ofthe internal carotid arteries, which effectively knocks out one of the brain hemi-spheres. While one of the hemispheres is anaesthetised, the patient is tested onsome cognitive tasks. Usually language and memory functions are evaluated. Thepatients response to the cognitive tasks is graded and after a period of rest the otherhemisphere is tested in the same way. Based on the left and right hemisphere testscores, a laterality index (LI) can be calculated as

LI =Scoreleft − Scoreright

Scoreleft + Scoreright

. (4.1)

The main drawback of the Wada procedure is its invasiveness, associated withsignificant risks for the patient. Furthermore, the procedure gives no informationon the functional organisation within the hemispheres.

It has been suggested that fMRI could provide a non-invasive method for theevaluation of language lateralisation. The most common procedure when deter-mining hemispheric language dominance with fMRI uses a combination of differ-ent language tasks to improve the detect-ability of language related areas [Ram-sey et al., 2001]. Then the laterality index is calculated as the difference of the

23 4.4 Brain Plasticity

number of activated voxels in the left and right hemisphere respectively dividedby the total number of activated voxels, which is equivalent to LI in Eq. (4.1). Tofurther increase the robustness of the laterality estimation, the analysis is usuallylimited to predefined regions of interest.

Several studies have been performed that compare the outcome of Wada test-ing and fMRI [Desmond et al., 1995; Binder et al., 1996; Yetkin et al., 1998a;Springer et al., 1999; Rutten et al., 2002b,a; Sabbah et al., 2003; Borjesson et al.,2004; Baciu et al., 2005]. In general, good agreement between results from Wadatesting and fMRI based LIs was observed. In addition, fMRI provides more detailson the functional organisation of the functions tested. This information may beuseful for determining the boundaries of resection. These studies have confirmedthat fMRI is a useful complement, or even a possible replacement of the Wadatest. The reproducibility of the results has been shown to be satisfactory, makingit feasible to make clinical decisions based on the fMRI results.

4.3.1 Source LocalisationEEG of epilepsy patients often exhibits spikes or spike wave bursts that are notaccompanied by clinical manifestations. Theses spike bursts are nevertheless im-portant for diagnostic purposes. They are caused by abnormal spiking activity inpopulations of hyper-synchronous neurons. These regions of hyper-synchronoushyperactivity are probably causing the abnormal neuronal activity causing theepileptic seizures. These epileptic sources can be reliably localised by simulta-neously collecting EEG and fMRI data [Benar et al., 2006; Zijlmans et al., 2007].By correlating the fMRI data with the EEG time-course, it is possible to iden-tify the source of the ictal sources and thereby provide information for improvedsurgical planning and procedure.

4.4 Brain PlasticityThe location of various brain functions may change with disease or injury. Ithas been thought that the young developing brain has a large potential for plasticchanges. However, an fMRI study of children suffering from hemiplegia chal-lenges this view, concluding that factors other than age dominate the potential foradaptive changes in the functional organisation [Holloway et al., 2000]. Severalstudies in patients after stroke have confirmed that areas of intact brain tissue arerecruited in the motor cortex ipsilateral to the hand moved. Johansen-Berg andcolleagues combined transcranial magnetic stimulation (TMS) and fMRI to as-sess whether the greater activity represents functionally important changes or not[Johansen-Berg et al., 2002]. For patients, TMS over ipsilateral motor cortex sig-


nificantly slowed the movement reaction time. For healthy controls there was nosignificant effect, thus increased functional activity must contribute to the functionof the affected limb.

A very exciting extension of this concept is to study functional re-organisationin response to rehabilitation. Specific brain regions may show altered functionalactivity correlated with improved clinical results after treatment. The initial pat-tern of functional activity after stroke, and the fMRI activation alterations inducedby initial therapy may be used as markers for the potential of functional recovery[Ward et al., 2003b,a; Dobkin et al., 2004]. Thus, fMRI results could be usedas a predictor for how much the patient will benefit from rehabilitation training.Based on the results of fMRI examinations, it may also be possible to tailor therehabilitation strategy to be optimal for the individual patient.

5AIMS

As noted in Chapter 1, Introduction, fMRI research includes a wide range of pro-cedures, as exemplified by the fMRI examination chain. All parts of the fMRIexamination chain are important for the outcome of the experiment. The goal ofthis work has been to increase the success rate of clinical fMRI examinations byproviding insights to important issues arising in all parts of the clinical fMRI ex-amination chain. This broad aim has been divided into a few specific questionsrelated to a specific problem or question in the examination chain.

The first question was whether diazepam, a common sedative, influences theoutcome of fMRI examinations. This question originates from the observationthat, due to claustrophobia or anxiety, some fMRI patients need to be pre-medicatedwith diazepam to be able to complete the examination. Since the sedation altersthe behaviour of the patient, it seems plausible that the results of an fMRI exam-ination may potentially be severely affected, even invalidated, by the administra-tion of a sedative. This issue was investigated in PAPER I.

The second aim was related to the presentation of visual stimuli. Two differenttechniques for presentation of visual stimuli are used in fMRI, projection screenand video goggles. The question then arose whether the results of fMRI exami-nations depend on the technique used to present the visual stimuli. This was thetopic of PAPER II.

The third question was related to the acquisition of fMRI data. Since its in-ception in 1992, two-dimensional (2D) gradient echo echo-planar-imaging (GRE-EPI) has been the number one imaging sequence for fMRI. With the advent ofparallel imaging it became possible to collect 3D BOLD weighted data at a highenough rate for fMRI. The question was then if a 3D parallel imaging technique(PRESTO-SENSE) could offer better fMRI properties than the traditionally used2D imaging sequences (GRE-EPI). This topic is explored in PAPER III.

Chapter 5. Aims 26

The fourth research aims was to investigate the analysis methods used forfMRI data. A specific question arose whether spatially adaptive filtering improvesthe analysis of fMRI data, a subject explored in PAPER IV.

The fifth aim related to the interpretation of fMRI examination results. Per-formance of different acquisition methods and analysis are usually investigatedusing some signal processing measures such as image SNR, activation volumeor explained variance. It is then of importance to know whether these measuresrelate to the subjective data quality perceived by the clinician. This subject isinvestigated in PAPER III

In summary, the specific aims of this thesis were:

Aim 1 To investigate whether diazepam administration prior to fMRI examina-tions influences the outcome of the fMRI examination.

Aim 2 To investigate whether the results of fMRI examination of visual cortexare affected by the visual stimulus modality.

Aim 3 To investigate whether fMRI using the PRESTO-SENSE imaging sequencecan provide better results than GRE-EPI.

Aim 4 To investigate whether spatially adaptive filtering improves the results ofthe fMRI analysis.

Aim 5 To investigate the conformity of subjective assessment of diagnostic qual-ity of fMRI results and objective measures of fMRI data quality.

Part II

Methods, Results and Discussions

29

The content of this part of the thesis will closely follow the imaging pipelinedescribed in Chapter 1, Introduction, while addressing the specific research ques-tions stated in Chapter 5, Aims.

The research questions that were declared in Chapter 5, Aims, will be coveredsequentially. For each question, more detailed background to the specific researchquestion is provided, followed by a short section describing the research methodsand the most important results. Then a discussion based on the research resultsand the literature will follow. The backgrounds and discussions presented herewill partially overlap with the presentations in the included papers. For moredetailed descriptions on materials, methods and results, the reader is directed tothe included papers.

6PATIENT PREPARATION

This chapter will address a specific problem regarding patient preparation forfMRI examinations, namely the issue stated as Aim 1: are fMRI results validwhen an anxiolytic is administered prior to scanning? The text is is based on thebackground, results and conclusions in PAPER I. Some extensions to the back-ground material and conclusions in the paper are made, which to some extent arebased on related publications not included in the thesis [Soderfeldt et al., 2006;Ragnehed et al., 2007].

6.1 Anxiolytics and fMRI

6.1.1 BackgroundMany common substances affect the BOLD response in various ways. For in-stance caffeine is well known for increasing the magnitude of the BOLD response[Mulderink et al., 2002], thus offering the patient a cup of coffee prior to scanningis considered to be beneficial. However, others have noticed that the effect of caf-feine on the BOLD signal depend on the level of chronic caffeine intake. IncreasedBOLD signal has been observed for high users whereas a reduced BOLD magni-tude was observed for low users [Laurienti et al., 2002]. Several studies have triedto explore this effect in more detail, and it has been shown that the BOLD tem-poral dynamics are altered by caffeine administration [Liu et al., 2004; Behzadiand Liu, 2006; Liau et al., 2008]. Other studies have shown that for instance alco-hol and heroin reduce the extent of activation to visual stimuli [Sell et al., 1997;Levin et al., 1998].

Benzodiazepines, such as diazepam, are a class of psychoactive drugs withvarying hypnotic, sedative, anxiolytic, muscle relaxant and amnesic properties,

Chapter 6. Patient Preparation 32

which are slowing down the central nervous system. Benzodiazepines are usefulin treating anxiety, insomnia, agitation, seizures, and muscle spasms. Benzo-diazepines bind to the GABA receptors and increases the affinity of the recep-tor to the GABA neurotransmitter, effectively increasing the inhibitory effect ofthe available GABA, producing sedatory and anxiolytic effects [McKernan et al.,2000].

In clinical practice, fMRI patients sometimes feel worried or anxious aboutthe examination, sometimes to the extent that they find it difficult or even impos-sible to undergo the examination. In such cases it is common to give the patienta small dose of diazepam to alleviate the patients discomfort. Even though ithas not been well known how the fMRI results are affected, diazepam has beenroutinely administered to worried patients. In a study by Kleinschmidt et al.theeffect of diazepam on the baseline BOLD signal was studied [Kleinschmidt et al.,1999]. They found that diazepam administration did not alter the magnitude ofthe baseline BOLD signal. However, more important is to know what happensto the activation induced BOLD response when diazepam is administered. Somehints may be obtained from studies on other benzodiazepines. For instance it wasshown that lorazepam administration attenuated amygdalar and insular activityin emotion processing, whereas activity in the pre-frontal cortex was unaffected[Paulus et al., 2005]. Further, the benzodiazepine midazolam reduced the activityfor anticipation of painful stimuli, primarily in the insula [Wise et al., 2007].

Given the anxiolytic and sedative effects of benzodiazepines the down regula-tion of limbic activity seem appropriate. Hence, it appears as if benzodiazepinesdo not have a significant influence on the BOLD signal in cortical areas. Onthe other hand, it also known that single doses of diazepam are capable of caus-ing significant decreases in performance, such as longer reaction time, decreasedeye-hand coordination, impaired information retrieval and reduced cognitive skills[Gier et al., 1981; Friedman et al., 1992; Kozena et al., 1995; Drummer, 2002].These are all effects that could influence the outcome of an fMRI examination, es-pecially since clinical fMRI examinations most often consider motor and languageretrieval functions [Sunaert, 2006; Stippich, 2007]. If the results of a clinical fMRIexamination do not represent the patient’s normal brain function, decisions basedon the fMRI results may be erroneous, resulting in sub-optimal treatment. Inworst case this could lead to loss of cognitive functions which may severely limitthe patient’s quality of life.

6.1.2 MethodsTo answer the question whether the results of a clinical fMRI examination wouldsuffer from administration of diazepam, twenty volunteers were recruited for afMRI study. The participants underwent two fMRI examinations and received ei-

33 6.1 Anxiolytics and fMRI

ther a placebo or the active substance (diazepam, 5 mg) about 30 minutes priorto the examinations. The administration of the substances was random, counter-balanced and double-blind. The fMRI sessions included a motor task, a languagetask and a working memory task. The fMRI data were evaluated to assess sub-stance related differences in activation patterns and activation volumes as well asdifferences in BOLD dynamics in primary functional areas using ANOVA.

6.1.3 ResultsThe main findings of the study were

I Significant BOLD activity was found in relevant areas for all tasks. Therewere no significant differences in the activation patterns between the phar-macological conditions for any of the tasks.

I There were no significant difference in activation volume or lateralisationbetween the pharmacological conditions for any of the tasks.

I No significant differences in BOLD dynamics between the pharmacologicalconditions were found.

6.1.4 DiscussionOther fMRI studies on the effects of diazepam on human behaviour have founddown-regulation of the functional activity of subcortical areas [Paulus et al., 2005;Wise et al., 2007]. In the present study, administration of diazepam induced nosignificant effects on the fMRI results or the BOLD dynamics. However, it shouldbe realised that the BOLD dynamics analysis was restricted to primary functionalareas (motor cortex and Broca’s area only), which means that there could be dif-ferences in secondary and supporting areas.

In conclusion, it was demonstrated that a clinically relevant low dose of di-azepam administered prior to fMRI examination had no statistically significanteffects on the activation results. It was also shown that there was no effect onthe temporal dynamics of the BOLD response [Ragnehed et al., 2007] in pri-mary functional areas. In combination with previous fMRI results, demonstrat-ing that Benzodiazepines primarily affect subcortical areas [Paulus et al., 2005;Wise et al., 2007], there is considerable evidence supporting that mapping of pri-mary motor and language functional areas is feasible after the administration oflow doses of diazepam.

7DATA ACQUISITION

Two different aspects of the data acquisition will be covered. First the impact ofstimulus modality on the outcome of the fMRI experiment is explored. Next acomparison of some important characteristics of different fMRI data acquisitionmethods is presented. This section is primarily based on the background, resultsand conclusions in PAPER II and PAPER III and specifically concern Aim 2: ismapping of visual cortex affected by stimulus modality, and Aim 3: is PRESTO-SENSE able to improve on EPI results.

7.1 Visual Stimulus DeliveryThis section is primarily related to Aim 2 and PAPER II.

7.1.1 BackgroundFor clinical usage of fMRI, accurate and reproducible results are utterly impor-tant. Decisions based on invalid results could have a huge negative impact onthe individual patient. Also, to study rehabilitation processes, brain plasticity andfunctional changes due to neurological disturbances, the results need to be repro-ducible over time. One limiting factor in this aspect is the stimulus modality. If theapplied stimuli varies in an uncontrolled manner between fMRI session it shouldbe expected that the results also vary in unpredictable ways, rendering it difficultor even impossible to make informed interpretations of the results.

Regardless of the specific fMRI task being used, some instructions and/orstimuli have to be delivered to the patient or subject. The stimuli and instruc-tions can be of different kinds, instructions can be auditory or visual in the form

Chapter 7. Data Acquisition 36

of text, images or even video. The stimuli used depends primarily on the task tobe used. Stimuli can also be delivered in several ways, it could be direct sensorystimulation, oral or visual cues marking the start or end of a task period or forinstance some test to be read.

In the case of sensory stimulation, there are essentially two different kindsof setups available, either manual application of the stimuli by a staff personbeing inside the scanner room or by remote application using a computer con-trolled equipment. There are several examples of different types of remotely con-trolled stimulus equipment, such as pneumatic [Briggs et al., 2004; Weibull et al.,2008a] and mechanical devices for tactile stimuli. Also electrical stimuli devices[Deuchert et al., 2002] have been used.

For the delivery of visual information, whether direct visual stimuli or task in-structions for mapping of higher cognitive functions, there are two alternatives forstimulus delivery. The stimulus is either projected onto a projection screen, posi-tioned within the scanner room, which is viewed via a mirror mounted onto thehead coil or by using one of the commercially available MR-compatible gogglesystems.

7.1.2 MethodsTo test whether stimulus modality influenced the fMRI results, visual stimuli wasdelivered to a test subject using projection screen and video goggles. The samevisual stimuli was presented to the test subject during five fMRI sessions usingthe projection screen and five sessions using the MR goggles. The results wereanalysed to assess the consistency of results over time, and for differences betweenstimulus modality.

7.1.3 ResultsThe main difference between the two visual stimulus modalities was the extent ofthe visual stimuli. For the projection screen the view angle of the stimuli was 5◦

and for the goggles the view angle was 20◦. As expected, this difference trans-lated into different visual cortex activation volumes; the goggles induced a largeractivation volume than the projection screen. Even though there was a decreasein activation volume over time, no significant differences in activation pattern oc-curred between the sessions. A very important and surprising difference in theactivation patterns from the two stimulus modalities was observed, the projectionscreen stimuli failed to activate the central part of the visual cortex, see Figure 7.1.

37 7.2 Imaging Sequences

FIGURE 7.1. Activation patterns in response to visual stimuli. Note the lackof activation in the central visual cortex when using projection screen.[Reprinted with permission from Magnetic Resonance inMedicine, Copyright c©2005. Elsevier Inc.]

7.1.4 DiscussionFor clinical usage of fMRI accurate and reproducible results are utterly impor-tant. Decisions based on invalid results could have a huge negative impact for theindividual patient. One requisite for accurate and reproducible results is properstimulus presentation. It was shown that presentation of visual stimuli using pro-jection screen viewed via a head coil mounted mirror could results in unexpectedactivation patterns. The lack of activation in the central part of the visual cortex isprobably due to physical disturbance of the visual stimuli, caused by the physicaldesign of the head coil. As the projection screen is viewed via a mirror mountedon the head coil, the visual pathway from the screen to the eyes is partially blockedby the head coil.

For some applications, such as surgical planning, absence of activation dueto poor stimulus presentation could have fatal consequences for the individualpatient. Apart from mapping of the visual cortex, applications that rely accuratepresentation of visual stimuli could also suffer from the obstruction of the visualfield. Also tasks highly demanding on cognitive skills and attention might sufferif the subject is distracted by the inferior visual presentation.

In conclusion, it was shown that video goggles are superior to projectionscreens regarding accurate mapping of the visual cortex.

7.2 Imaging SequencesThis section tries to answer the question of Aim 3 and is based on PAPER III.


7.2.1 BackgroundProper data acquisition is an essential part of the general medical imaging ex-amination chain. In order to successfully perform fMRI, the selected imagingsequence must be sensitive to the BOLD effect, and it should also provide suffi-cient temporal SNR (tSNR) [Murphy et al., 2007]. To provide accurate enoughfunctional localisation, it is also important that the collected images do not sufferfrom extensive geometrical distortions.

Two-dimensional Gradient Echo - Echo Planar Imaging (2D GRE-EPI) is themost widely used acquisition sequence for BOLD fMRI. By the use of 2D GRE-EPI, full brain coverage with reasonable spatial resolution (3 × 3 × 3 mm3) canbe obtained within two seconds, which enables a robust and accurate mappingof motor or sensory cortex in about 3 minutes if a block design is utilised. Themain drawbacks of 2D GRE-EPI are geometrical distortions, signal drop-out andhigh acoustic noise levels [de Zwart et al., 2006]. Due to geometrical distortions,it is difficult to make correct registration with the corresponding high resolutionanatomical images. Consequently, detailed neuro-anatomical identification of ac-tivated areas is sometimes not feasible. In areas close to brain-air interfaces, forinstance around the internal auditory canals, one may often encounter a completesignal loss due to magnetic field heterogeneity, making it impossible to map anyfunctional activity in those areas.

Parallel imaging techniques, such as SENSE (sensitivity encoding) [Pruess-mann et al., 1999], which simultaneously acquire MRI data from two or morereceiver coils, can be used to increase temporal or spatial resolution. EPI imagingartifacts, such as geometrical distortions and signal dropouts, can also be reducedby the use of parallel imaging [de Zwart et al., 2006]. Combining parallel imag-ing with three-dimensional (3D) image acquisition, such as PRESTO (principlesof echo-shifting with a train of observations)[Golay et al., 2000], enables wholebrain data acquisition in less than one second. However, parallel imaging gener-ally reduces the image SNR. Fortunately, fMRI experiments are usually limitedby temporal SNR (physiological noise and scanner instabilities), not image SNR,leading to a limited penalty for reduced image SNR [Kruger and Glover, 2001].

Decisive factors for detection of activation using fMRI are tSNR, the num-ber of scans (N), and task induced BOLD amplitude given by the percent signalchange (PSC). The statistical significance of task induced signal change is propor-tional to √

N × TSNR× PSC (7.1)

(adopted from Murphy et al. [2007]. From Eq. (7.1) it is clear that performance ofthe imaging sequences can be characterised by TSNR, effective TSNR (eTSNR =√N × TSNR ) and PSC extracted from functionally defined regions of interest.

39 7.2 Imaging Sequences

7.2.2 MethodsNine subjects were included in a study where fMRI data was collected using fourdifferent imaging sequences, 2D GRE-EPI, 2D GRE-EPI-SENSE, PRESTO andPRESTO-SENSE. The SENSE acceleration factor (R) was set to 2.9 for both EPI-SENSE and PRESTO-SENSE. Two paradigms were used, a motor paradigm anda listening paradigm, to induce brain activation in different parts of the brain. Thesame paradigms were used for all four imaging sequences. All fMRI data wereanalysed in Matlab using SPM5 and some custom routines. Identical analysisprocedures were used for all data. In order to assess differences in activationdetection ability, the fMRI data and the fMRI analysis results were examined inseveral ways. From the fMRI data the TSNR, eTSNR, number of activated voxels(#voxels) and PSC were extracted and from the fMRI analysis results the numberof activated voxels were extracted. Two-way ANOVA was used to evaluate thesignificance of the resulting performance measures.

7.2.3 ResultsThe analysis of the PSC showed that there were no significant differences in taskinduced signal changes between the acquisition methods. Since no PSC differ-ence was found between the acquisition methods, their performance is determinedby the eTSNR (see Eq. (7.1)). Image SNR was highest for the EPI sequence, fol-lowed by EPI-SENSE and PRESTO. Lowest image SNR was found for PRESTO-SENSE. The eTSNR, taking acquisition rate into account, was higher for theEPI-sequences than for the PRESTO sequences, but using this measure PRESTO-SENSE scored higher than PRESTO.

In summary, the evaluation of these descriptive performance measures showedthat the two EPI-sequences performed better than the PRESTO sequences and thatPRESTO-SENSE performed better than PRESTO at 1.5 T. The results are shownin Figure 7.2

7.2.4 DiscussionOther studies have found equal performance of EPI and EPI-SENSE for R = 2 anda slight performance drop with R = 3 [Preibisch et al., 2003], and that PRESTO-SENSE (R = 2) performs equal or better than PRESTO [Golay et al., 2000]. Com-parisons between EPI and PRESTO-SENSE (R = 2) have shown similar perfor-mance [Klarhofer et al., 2003]. A recent study at 3 T applied SENSE acceler-ation to PRESTO in two acquisition directions and found a significantly higherdetection power of the PRESTO-SENSE sequence over conventional EPI [Neg-gers et al., 2008]. In contrast, in PAPER III, we found that SENSE acceleration


FIGURE 7.2. Performance measures for the different imaging sequences on themotor and listening tasks. Similar relative performance of the acquisitionsequences are observed for the different paradigms. Significant (p < 0.05)results are indicated in the figure.

significantly increased the detection power of the PRESTO sequence and that theregular EPI sequence performed significantly better than PRESTO and PRESTO-SENSE. One possible explanation for these results may be the SENSE factorsused. In PAPER III, a SENSE factor of R = 2.9 was used, which is higher than thatused in the studies which found equal performance of EPI, EPI-SENSE, PRESTOand PRESTO-SENSE [Golay et al., 2000; Preibisch et al., 2003; Klarhofer et al.,2003].

Altogether, there is no evidence that the PRESTO-SENSE sequence performsbetter, regarding detection of activated voxels, than EPI at 1.5 T. Using an ac-celeration factor of R = 2, PRESTO-SENSE and EPI seem to perform equallywell regarding detection power. Given this finding, the higher acquisition rate ofPRESTO-SENSE may be beneficial for research questions where the dynamics ofthe BOLD response is important.

8ANALYSIS

The third step of the examination chain is to analyse the data that has been col-lected. Obviously the analysis step is as important as any other of the imagingexamination chain. Without accurate and reliable analysis methods the examina-tion results will not be of diagnostic value. For fMRI there is a large number offree and commercial analysis packages available. Some of the most well knownfMRI analysis packages are SPM, FSL and Brainvoyager. This section is basedon PAPER IV and results related to the question raised in Aim 4: ’can adaptivefiltering improve the fMRI analysis?’, are presented.

8.1 Statistical Analysis

8.1.1 BackgroundAmong the statistical methods used to detect brain activity using fMRI, the Gen-eral Linear Model (GLM) [Friston et al., 1995; Worsley and Friston, 1995] is byfar the most common. Lately, canonical correlation analysis (CCA), originally de-veloped by Hotelling [1936], has received substantial attention in the neuroimag-ing community [Friman et al., 2001, 2002a,b, 2003; Nandy and Cordes, 2003b,a;Rydell et al., 2006]. A promising alternative to GLM analysis is the restrictedCCA (rCCA) based analysis method presented by Friman et al. [2003], whichextends the GLM by allowing adaptive spatial filtering. The analysis procedurehas been proved to be effective using simulated fMRI data. However, the rCCAmethod had only been validated using simulated data and data from a quite simplemotor task calling for more elaborate validation.

A useful way of estimating the efficiency of diagnostic tests is the ReceiverOperating Characteristic (ROC) [Constable et al., 1995; Skudlarski et al., 1999].

Chapter 8. Analysis 42

Using ROC in the fMRI context, either the exact locations of truly activated voxelshas to be known, or simulated data has to be used. However, when using real data,it is impossible to know exactly which voxels are truly active. This explains thefrequent use of simulated data for evaluation of different fMRI analysis methods.One significant limitation when using simulated fMRI data is the difficulty toresemble both the spatial and temporal correlations that occur in real fMRI data.This problem is overcome by the modified ROC (mROC) method introduced byNandy and Cordes [2003a]. This method requires that a resting state data set isacquired in addition to the activation data. Then the resting state data is analysedas if it were an activation data set. For any given threshold there should be fewervoxels declared active for the resting state data than for the activation data. Usingthe mROC method, the performance of the analysis method is assessed by plottingthe proportion of activated voxels for the activation task against the proportion ofactivated voxels for the resting state data, resulting in an ROC-like plot. Analysisperformance is characterised by the mROC plot, a well performing method willresults in an ROC curve close to the upper left corner or, equivalently, by a largearea under the curve. In fMRI the number of false positives is kept low. Thereforeit is advisable to use the area under a part of the mROC curve (auROC) where thefalse positive fraction is low as a performance measure.

8.1.2 MethodsThe mROC method [Nandy and Cordes, 2003b] was used to compare the rCCAmethod of Friman et al. [2003] with the GLM analysis represented by an F-testin SPM2. The comparison was done using fMRI data from eight volunteers. ThefMRI data included resting state data and data from a language task. Identicalanalysis procedures were used for the language data and the resting state data.The area under the part of the mROC curve where the false positive fraction islower than 0.01 was used as a measure of the analysis performance.

The rCCA method utilises adaptive spatial smoothing, which means that thefilter kernel will vary in size and orientation depending on the local data properties(for details see [Friman et al., 2003; Rydell et al., 2006]). For the GLM analysis,two different spatial smoothing kernels (4 and 6 mm FWHM, referred to as F4 andF6) were used to prevent bias due to differently sized spatial smoothing kernels.

8.1.3 ResultsThe resulting mROC curves are shown in Figure 8.1, and the auROC values arelisted in Table 8.1. Analysis of the rCCA smoothing kernels revealed that theaverage effective size of the adaptive filters was larger than the 4 mm FWHM, butsmaller than the 6 mm FWHM Gaussian filters.

43 8.1 Statistical Analysis

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FIGURE 8.1. mROC curves for the GLM and rCCA analyses. The dashed,dot-dashed, and solid lines correspond to F4, F6, and rCCA analyses,respectively. Note that the rCCA analysis (solid line) received the high-est scores for all but one data set. [Reprinted with permissionfrom Journal of Magnetic Resonance Imaging, Copyrightc©2008. Wiley-Liss, Inc.]

8.1.4 DiscussionTwo very important factors to determine the performance of an analysis tool aresensitivity and specificity. ’Sensitivity’ refers to the ability to identify activatedvoxels, whereas ’specificity’ refers to the ability to distinguish between truly ac-tivated voxels and false activations. For the first time, the rCCA method byFriman et al. [2003] was systematically compared with the widely used GLManalysis using a modified ROC method [Nandy and Cordes, 2003b]. Combinedwith previous evaluations using synthetic data, the results showed that the rCCAmethod is very competitive.

One drawback of the mROC method used is that large spatial filter kernelswill, in general, result in higher scores. There are two reasons for this: (1) onthe resting state data, a large spatial filter will generally suppress false activations,since they are mostly relatively small areas; (2) the activation data will most likelyhave extended regions of activity and a large smoothing kernel will tend to extendthose regions even more, resulting in an extended number of true positives. Bothof these effects will tend to increase the area under the mROC curve.

This issue makes the interpretation of the rCCA results somewhat more diffi-cult. Since the spatial filter size and orientation depend on the properties of the

Chapter 8. Analysis 44

auROC Normalised auROC

Subject F4 F6 rCCA F4 F6 rCCA1 9.93 10.53 17.23 1.00 1.06 1.742 1.34 1.58 1.73 1.00 1.18 1.293 1.10 1.16 2.58 1.00 1.05 2.354 19.63 23.65 33.75 1.00 1.20 1.725 5.15 6.16 7.50 1.00 1.20 1.466 16.07 19.07 17.58 1.00 1.19 1.097 16.43 19.82 21.69 1.00 1.21 1.328 8.77 11.03 11.34 1.00 1.26 1.29

Mean 9.80 11.63 14.18 1.00 1.17 1.53

TABLE 8.1. Table of auROC measures. The rCCA method scored highest in allcases but one.

voxel neighbourhood, the size of the filter varies across the images. Thus, it is dif-ficult to predict how the spatial filtering will affect the mROC curve. If the rCCAconsistently uses a smaller filter for the resting state data than for the activationdata it is likely that the auROC would indicate a worse performance than the ac-tual. Analysis of the filter kernels selected by rCCA showed that the filter kernelsize did not systematically differ between the rest and activation data, indicatingthat the results are not severely biased by filter sizes.

Several approaches to spatially adaptive filtering has been introduced, and theyare all able to suppress noise while preserving the shape of the activated region[Friman et al., 2003; Walker et al., 2006; Tabelow et al., 2006; Rydell et al., 2007;Monir and Siyal, 2009]. Although different, these methods share some commoncharacteristics. For instance, they use a measure of local signal similarity to con-trol the spatial filter and some are capable of taking local anatomical informationinto account [Walker et al., 2006; Rydell et al., 2007]. These spatial adaptive fil-tering methods have all been shown to increase functional SNR, while preservingdetails and anatomical specificity of the activation patterns. Another common fea-ture is that validations were performed using synthetic data, and that examples ofreal data were provided.

Altogether there is convincing evidence that spatially adaptive filtering canimprove specificity of fMRI analyses, at the expense of computational demandand model complexity.

9INTERPRETATION OF FMRI

RESULTS

In this section the focus will be on the fourth part of the examination chain; the useof fMRI results to make a diagnosis or decision regarding surgery. The results inthis chapter are mainly based on PAPER III, PAPER IV and to some extent PAPER

V. And the aim was to resolv the issues stated in Aim 4: does adaptive filteringimprove the fMRI analysis? and Aim 5: are objective and subjective measures offMRI quality concordant?

9.1 Objective and Subjective MeasuresThis section, which is based on the results from PAPER III and some unpublishedresults, tries to resolve the issue stated in Aim 5.

9.1.1 BackgroundVital for clinical usage of fMRI is whether the diagnostic quality of the functionalmapping is sufficient to make correct decisions. Many factors influence the end re-sult of fMRI examinations, including choice of imaging sequence, pre-processingstrategy, analysis method and parameters. The impact of these factors is usuallyinvestigated using various signal characteristics such as image SNR, explaineddata variance or activation volume. A most important question when it comes tothe clinical use of fMRI is whether there is any difference in diagnostic qualityof the functional mapping results from different imaging sequences or analysisprocedures. It is important to realise that high scores on descriptive image mea-sures, like SNR or fMRI activation volume, are not necessarily indicators of high

Chapter 9. Interpretation of fMRI Results 46

diagnostic quality of the results. For diagnostic purposes, correct localisation ofthe activation is certainly more important than a large activation volume.

9.1.2 MethodsFour imaging sequences (EPI, EPI-SENSE, PRESTO and PRESTO-SENSE) wereused to collect fMRI data on a motor task for nine volunteers (Section 7.2, ImagingSequences). All data were identically pre-processed and analysed. Five reviewerswere recruited to perform Visual Grading (VG) of the fMRI results. The diagnos-tic quality of the fMRI results were evaluated using Visual Grading Characteristics(VGC) [Bath and Mansson, 2007]. The VGC results were then compared with thedescriptive measures presented in Section 7.2, Imaging Sequences.

For the VG procedure, the reviewers were instructed to select the statisti-cal threshold they found was the optimal, see Figure 9.6, and then to grade thefunctional results using three different image quality criteria. The criteria wereselected to express different aspects of functional image quality, more specifi-cally, global quality, large-scale neuroanatomical correspondence, and local neu-roanatomical correspondence. Each criterion was graded on a scale from 1 (verypoor) to 5 (very good). The score for global quality was selected to reflect the in-cidence of false activations, such as activations in ventricles or veins. Large-scaleneuro-anatomical correspondence was intended to describe the quality of the over-all activation pattern, reflecting detection of cortical areas known to be involvedin motor tasks, such as primary motor cortex and supplementary motor areas, aswell as activations in areas not related to the task. Regional neuro-anatomicalcorrespondence was designed to indicate the neuro-anatomical correctness of theactivation in primary motor cortex.

Relative performance on the of the imaging sequences regarding the diagnosticquality was assessed using VGC analysis [Bath and Mansson, 2007]. This analysisprocedure has previously been successfully applied to certain radiological data[Vikgren et al., 2008]. In VGC analysis, the relative performance of two imagingmodalities (A and B) is assessed by plotting the proportion of fulfilled criteria ofA versus B. If the area under the resulting line is 0.5 (diagonal) the two methodswere rated equally good and if the area is larger than 0.5 (above the diagonal),then method A performed better than B.

9.1.3 ResultsIt was observed that the threshold selected by the observers varied with imagingsequence, see Figure 9.6. Even though the inter-observer variance is not negligi-ble, it can be seen in Figure 9.6 that for each subject there is a common patternin the threshold selection. In Figure 9.1, each image sequence’s average rating

47 9.1 Objective and Subjective Measures

(over subject and observer) and the VGC curves are shown, from which it is clearthat the EPI sequence received the highest ratings and that there was virtuallyno difference in the rating of the other imaging sequences. This is in contrastwith the descriptive measures where there were significant differences betweenboth EPI-SENSE and both PRESTO sequences as well as between PRESTO andPRESTO-SENSE.

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FIGURE 9.1. Average rating over subjects and reviewers for all imaging se-quences.

9.1.4 DiscussionWhen comparing the subjective ratings in Figure 9.1 with the objective measuresin Figure 7.2, there are some differences but also some commonality. For one,the EPI sequence received the highest scores on both subjective and objectivemeasures. In addition, the PRESTO-SENSE sequence scored higher than thePRESTO sequence on both types of measures; however, the difference was notsignificant for the subjective measures. The main difference between the subjec-tive and objective measures was that the EPI-SENSE sequence obtained evidentlylower scores compared to EPI on the subjective measures, while it was scoredsimilar to the EPI sequence using the objective measures.

Another interesting observation was that the thresholds selected by the ob-servers showed obvious similarity with respect to the #voxels and eTSNR mea-sures in Figure 7.2. Lower thresholds were selected when the eTSNR was low.This indicates that the observers were adjusting the thresholds to account for dif-ferences in detection power.


In conclusion, objective and subjective performance measures exhibit somedifferences that indicate that objective measures such as eTSNR or #voxels arenot sufficient to determine the diagnostic quality of fMRI results. This resultis probably not only applicable to evaluations of imaging sequences, but also tocomparisons of different analysis methods.

9.2 Significance Testing of RCCA Results

9.2.1 BackgroundIn order for a statistic value, for instance a t-value, or a correlation coefficientresulting from the analysis of fMRI data, to have meaning, it has to be estimatedhow likely it is that the statistic value could have been obtained by chance alone.Significance testing is the process of estimating the probability that a statisticvalue is exceed by chance alone. For most statistical methods there exist knowndistribution functions for the statistic values, such as the Student’s t-distributionfor the t-test or the normal distribution for correlation coefficients (for large sam-ples). The significance is determined by calculating the percentile correspondingto a specific statistic value.

For rCCA correlation coefficients, there is no known distribution, thus us-age of rCCA has been hampered by the lack of a practical thresholding tech-nique. Until now, one has been limited to methods that rely on additional dataacquisition [Nandy and Cordes, 2007] or time-consuming resampling techniques[Bullmore et al., 2001; Laird et al., 2004]. In PAPER IV, a novel, completelydata-driven, significance testing method that does not rely on any extra data ac-quisitions was presented. The method is an adaption of a method described byBaudewig et al.for ordinary correlation analysis [Baudewig et al., 2003].

9.2.2 Methods and ResultsIn ordinary canonical correlation analysis, the hypothesis that the largest correla-tion coefficient (r1) is non-zero can be tested using the fact that the Wilks lambdastatistic is approximately chi-square distributed,

W = − (N − 0.5(n1 + n2 + 3))

p−1∑i=2

ln(1− r2i ) ∼ χ2. (9.1)

However, for rCCA, the introduction of restrictions on the canonical coeffi-cients make it likely that the shape of the Wilks lambda distribution will deviatesomewhat from the chi-square distribution. Thus, a more flexible distribution is

49 9.2 Significance Testing of RCCA Results

FIGURE 9.2. Fitting of chi-square and gamma distributions to Wilks lambdastatistics from resting state data. The gamma distribution provide aequal or better fit than the chi-square distribution. The dashed verticallines in the magnified view mark the 95th, 99th, and 99.9th percentiles.[Reprinted with permission from Journal of MagneticResonance Imaging, Copyright c©2008. Wiley-Liss, Inc.]

needed to describe the data accurately. The chi-square distribution is a specialcase of the gamma distribution,

χ2(n) = Γ(n/2, 2), (9.2)

where n is the degrees of freedom. Thus the gamma distribution could be anappropriate approximation for the distribution of Wilks lambda for the rCCA cor-relation coefficients.

To test whether the gamma distribution could approximate the null distribution


FIGURE 9.3. Gamma distribution fitted to Wilks lambda statistics for rCCAanalysis of a language task. The tail of the distribution is not as wellapproximated as for the resting state data, due to the task inducedover-representation of large correlation coefficients. The dashed verticallines in the magnified view mark the 95th, 99th, and 99.9th percentiles.[Reprinted with permission from Journal of MagneticResonance Imaging, Copyright c©2008. Wiley-Liss, Inc.]

of Wilks lambda statistics, resting state fMRI data was analysed using rCCA asif it was activation data. Wilks lambda statistics were calculated from the resultsand a chi-square and gamma distributions were then least squares fitted to thehistogram of Wilks lambda statistics, see Figure 9.2. The gamma distributionprovided a good approximation of the null distribution for all data sets.

Next the gamma distribution was used to approximate the distribution of rCCAstatistics from analyses of a fMRI language paradigm. Again, the gamma distribu-

51 9.2 Significance Testing of RCCA Results

tion provided a reasonable approximation of the underlying data, see Figure 9.3.An important observation was that the tail of the activation data distributions werenot as well described as for the resting state data. This is a desired feature and iscaused by the over-representation of high correlations induced by the behaviouraltask.

By calculating the correlation coefficient corresponding to a particular per-centile of the fitted gamma distribution, a threshold can be determined that con-trols the number of false positives. For instance by finding the correlation coeffi-cient that corresponds to the 99th percentile, the probability is 1% that a correla-tion coefficient exceeds this value. This is described by Figure 9.4.

percentile

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FIGURE 9.4. Magnified view of the tail of a typical histogram of Wilks lambdastatistics and a fitted gamma distribution. The dashed line indicate the95th, 99th, 99.9th and 99.99th percentiles and the corresponding corre-lation coefficients are also displayed. [Reprinted with permissionfrom Journal of Magnetic Resonance Imaging, Copyrightc©2008. Wiley-Liss, Inc.]

9.2.3 DiscussionIn PAPER IV it was shown that adaptive spatial filtering combined with rCCAperforms better than conventional GLM analysis. One factor that has limited theuse of rCCA is the absence of an appropriate significance estimation method. Inresponse to this issue, a completely data driven significance estimation methodthat adapts itself to the underlying data was introduced in PAPER IV. The methodwas shown to provide accurate control over the false positive rate. In contrastto other methods, the proposed significance estimation procedure does not suffer


from the need of extra data collection or time-consuming resampling calculations.The introduction of the significance estimation enables thresholding of rCCA-results to control the proportion of false positives. The latter makes comparisonsof activation maps from different subjects or scanning sessions possible.

9.3 Thresholding Issues for Clinical fMRI

9.3.1 BackgroundThe statistical analysis of fMRI data results in a statistic value, such as a cor-relation coefficient or a t-value, for each voxel. This statistic value is basicallyindicating the degree of similarity of the voxel time-course with a model of theexpected task-induced BOLD response. In order to discriminate between activeand non-active voxels, a threshold value, based on an error rate controlling pro-cedure, is determined [Marchini and Presanis, 2004; Logan and Rowe, 2004].Regardless of the method used to determine the statistic threshold for activations,the appearance of the resulting activation map strongly depend on the significancelevel (threshold value). For clinical usage of fMRI, this dependence on thresh-old selection is troublesome, since the interpretation of the fMRI results and thesubsequent clinical decision may be gravely influenced by the threshold selection.This section will provide some examples of problems arising due to the thresholddependence of the fMRI activation maps.

9.3.2 Lateralisation of Brain FunctionThe most basic approach to estimate the lateralisation of brain function usingfMRI results is to calculate the LI according to

LI =ΣleftX − ΣrightX

ΣleftX + ΣrightX(9.3)

where Σleft/right denote sum over all voxels in the respective hemisphere, and X isa voxel score. The common choice it to let X be either one or zero indicating ifthe voxel is considered to be active or not. Thus, the number of activated voxelsin the respective hemisphere is used to calculate the LI. To make the calculationa bit more specific it is common to restrict the analysis to use the number ofactivated voxels in one or more ROIs [Spreer et al., 2002; Szaflarski et al., 2002].This simple formulation suffers from one major problem. The result of the LIcalculation depends heavily on the statistical threshold used to define activatedvoxels, which is illustrated in Figure 9.5. Depending on the statistic threshold, thesame subject could be categorised as left dominant, right dominant or bilateral.

53 9.3 Thresholding Issues for Clinical fMRI

Thus, estimating functional brain lateralisation using a single statistic threshold isnot advisable.

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FIGURE 9.5. Lateralisation of brain functions is dependent on the statisticalthreshold used to define activity. In this case brain lateralisation of a lefthand finger tapping task was calculated for a number of different statisticalthresholds. Depending on the threshold selection, the subject could havebeen declared as either bilateral or left- or right- hemispheric dominant.

One way to reduce the threshold dependence of LIs based on a single thresholdis to introduce a weighting based on the statistical significance of the activatedvoxels. For instance, if X = − log(p) is used in Eq. (9.3) the results is muchmore stable over different thresholds [Ragnehed et al., 2004]. Another approachwas to calculate a (weighted) average of LIs calculated for several thresholds,

LI =

∑t t× LI(t)∑

t t, (9.4)

where t is the threshold value and LI(t) is the lateralisation index at thresholdt [Wilke and Schmithorst, 2006; Wilke and Lidzba, 2007]. This is the approachused to calculate LIs in PAPER V.

9.3.3 Subjective Threshold SelectionIn PAPER III the clinical quality of fMRI results was judged by experienced ob-servers. The observers were instructed to select the ’optimal’ threshold for clin-ical evaluation of the functional activity resulting from a motor task. The data


was collected using four acquisition methods, EPI, EPI-SENSE, PRESTO andPRESTO-SENSE. Interestingly, the thresholds selected by the observers followeda common pattern with the highest thresholds for the EPI sequences, the lowestthreshold for PRESTO and an intermediate threshold for PRESTO-SENSE. How-ever, the individual level of the thresholds differed considerably, see Figure 9.6.The evident inter-observer variation in the selection of an ’optimal’ threshold forclinical decisions is an indication that personal preferences are important to thethreshold selection. This indicates that a single activation map is not sufficient forclinical purposes.

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FIGURE 9.6. Each reviewer’s subjective selection of ’best’ statistic threshold forclinical purpose. For each subject there is clearly a common pattern inthe reviewers selection of the ’optimal’ threshold even though the individuallevels vary. E: EPI, E-S: EPI-SENSE, P: PRESTO, P-S: PRESTO-SENSE.

9.3.4 Presurgical fMRIIt has been suggested that there is a ’safe distance’ between an fMRI activation andsurgical target areas, where the risk for post-surgical deficits should be minimal[Yetkin et al., 1998b; Haberg et al., 2004]. This view has been rightly criticised[Sunaert, 2006]. One problematic aspect is that the apparent margin of an acti-vation is highly dependent on the applied statistical threshold. Thus, the distance

55 9.3 Thresholding Issues for Clinical fMRI

from an activation margin and any other anatomically defined point is inevitablyvarying with threshold selection. Again, this example shows that clinical deci-sions should not be made from a single thresholded fMRI activation map.

9.3.5 DiscussionSeveral examples in this section demonstrate the danger of using single thresh-olded results for clinical decisions. A minimum requirement is that the diagnos-tician has access to activation maps from several different thresholds. An evenbetter alternative is if the clinician has access to the full statistic map and is ableto interactively manipulate the threshold level to really explore the results. Onlyin this manner a fully informed decision can be obtained.

10SUMMARY

fMRI, especially clinical fMRI, has to be conducted with care to produce ade-quate results. Throughout the fMRI examination chain there are several pitfallsto avoid. In this thesis some important issues arising throughout the fMRI exam-ination chain have been explored. In summary, the following results have beenachieved:

Aim 1 To investigate whether diazepam administration prior to fMRI examinationsinfluences the outcome of the fMRI examination.

RESULT: Considerable evidence supporting that mapping of primary motor andlanguage areas are unaffected by small doses of diazepam was presented.Other studies have found modulating effects on sub-cortical areas. Thus,pre-medication with low doses of diazepam prior to fMRI have no significanteffect on the mapping of primary functional areas.

Aim 2 To investigate whether the results of fMRI examination of visual cortex areaffected by the visual stimulus modality.

RESULT: It was shown that activation in visual cortex was strongly dependenton the stimulus modality. If not handled properly, this could have a devas-tating effect on mapping of the visual cortex.

Aim 3 To investigate whether fMRI using the PRESTO-SENSE imaging sequencecan provide better results then EPI.

RESULT: Results were inconclusive on this issue. Poorer performance for thePRESTO-SENSE than for the EPI sequence was found on relevant measures,but others have found better performance for PRESTO-SENSE at higherfield strength.

Chapter 10. Summary 58

Aim 4 To investigate whether spatially adaptive filtering improves the fMRI anal-ysis results.

RESULT: There is collective evidence that adaptive spatial filtering can improvethe specificity of fMRI analysis, at the expense of higher computational de-mands and higher complexity of the analysis procedure.

Aim 5 To investigate the conformity of subjective assessment of diagnostic qualityof fMRI results and objective measures of fMRI data quality.

RESULT: It was shown that subjective ratings of fMRI quality are fairly similarto, but not fully in unison with, objective measures. Subjective measuresmay capture important aspects of fMRI results that are not reflected by ob-jective measures.

In conclusion, if properly designed and executed, clinical fMRI examinations canprovide essential information on functional localisation to aid clinical decision.The fMRI procedure is completely non-invasive and functional information ismade available prior to any surgical procedure. Thus, using fMRI, functionalinformation may be incorporated into the planning of surgical procedures, such astumour resection or temporal lobectomy of epilepsy patients.

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ACKNOWLEDGMENTS

Science is rarely a one mans works and this thesis is not an exception. Withoutthe help, enthusiasm and support from many of individuals this thesis would neverhave been completed.I express special thanks to:- my supervisors Peter Lundberg, Maria Engstrom, Orjan Smedby andBirgitta Soderfeldt for their never ending support and encouragements thathas kept me going till the end. They have also significantly contributed to thiswork by sharing their research ideas and experience.- my co-authors, who all have made important contributions to the works thatevolved into this thesis.- all past and present co-workers of the MR-research group at Division of Radi-ological Sciences, especially Olof, Anders, Vaclav, Anders, Helene,

Janne, Nils and Eva for making it a pleasant working place and a good re-search environment.- all past and present members of CMIV, especially Anders, Maria, Petter,

Andreas, Henrik, Petter, Marcel, Annika and Johan for the good at-mosphere and invaluable assistance.- Special thanks to my family, Annika, Ida and Emma for endless love and sup-port, and for keeping me focused on what is really important in life.

Thank you all!

Part III

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