Development of Simulator Training to Reduce Head Motion Artifact in fMRI

Shawn Michael Ranieri

A thesis submitted in conformity with the requirements for the degree of Master of Health Science in Clinical Biomedical Engineering

Institute of Biomaterials and Biomedical Engineering University of Toronto

Supervised by

Dr. Simon J. Graham

Department of Medical Biophysics

University of Toronto

©Copyright by Shawn Ranieri 2011

ii

Development of Simulator Training to Reduce Head Motion

Artifact in fMRI

Shawn Ranieri

Master of Health Science in Clinical Biomedical Engineering

Institute of Biomaterials and Biomedical Engineering

University of Toronto

2011

Abstract

Functional MRI (fMRI) is a primary tool in the study of brain function. The primary cause of

data corruption in fMRI is head motion while scanning. This problem is compounded by the fact

that subjects are asked to perform behavioural tasks, which can promote head motion. Random

and/or large head motions are often not handled well in post-processing correction algorithms.

This thesis investigates the use of an alternate method: an MRI simulator to help reduce head

motion in subjects through training. A simulator environment was developed where subjects

could be trained to reduce their head motion through closed loop visual feedback. The effect of

simulator training was investigated in young, old and stroke subjects. Performance of subjects

with respect to head motion was investigated prior, during and after feedback training, including

subsequent fMRI scans. This research helps improve fMRI image quality by reducing head

motion prior to scanning.

iii

Acknowledgments

I would like to thank Dr. Simon Graham for his indispensible guidance and knowledge in

supervising this work. To the thesis supervisory committee, Dr. Bradley MacIntosh and Dr. Tom

Schweizer, thank you for your advice and support throughout this project.

I would also like to thank all the members of the fMRI lab at the Rotman Research Institute, with

a special thanks to Annette Weekes-Holder and Tara Dawson for their invaluable help with the

project and for making the lab a welcome place for me. To Fred Tam, your resourcefulness

knows no limits, thank you. To Dr. Jon Ween, thank you for sharing your volunteer database. I

would also like to thank Dr. Shaun Boe for his contributions, including use of his task design and

pressure bulb hardware. Most importantly, I would like to show my gratitude to the volunteers,

all of whom were a pleasure to work with.

I would like to thank my friends and family for all their support. A special thanks to my mother

Jacqueline, who has supported me throughout my university career, and to my father Michael,

who is always with me.

Additional thanks are extended to the Institute of Biomaterials and Biomedical Engineering at

the University of Toronto. The Heart and Stroke Foundation of Ontario and the Natural Sciences

and Engineering Research Council of Canada are also thanked for providing funding support.

iv

Table of Contents

Acknowledgments .......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Figures ................................................................................................................................ vi

List of Tables ............................................................................................................................... viii

List of Abbreviations ..................................................................................................................... ix

1 Introduction ................................................................................................................................ 1

1.1 Statement of Research Problem .......................................................................................... 1

1.2 Specific Aims ...................................................................................................................... 3

2 Background ................................................................................................................................ 5

2.1 Functional MRI and the BOLD Effect ................................................................................ 5

2.2 Motion Artifact in Functional MRI ..................................................................................... 6

2.2.1 Subject Motion ........................................................................................................ 8

2.3 fMRI Simulator ................................................................................................................. 10

2.3.1 Training ................................................................................................................. 11

2.4 Limitations of Current Methods ........................................................................................ 12

2.4.1 Physical Restraint .................................................................................................. 12

2.4.2 Retrospective Coregistration ................................................................................. 13

2.4.3 Navigator Echoes .................................................................................................. 14

2.4.4 PACE .................................................................................................................... 14

2.4.5 External Monitoring .............................................................................................. 14

3 Development of Simulator Training to Reduce Head Motion Artifact in fMRI ...................... 17

3.1 Introduction ....................................................................................................................... 17

3.2 Methods ............................................................................................................................. 18

3.2.1 Simulator Hardware .............................................................................................. 18

v

3.2.2 Task Protocol ........................................................................................................ 20

3.2.3 Simulator Pilot Study ............................................................................................ 25

3.2.4 Cohort Study ......................................................................................................... 25

3.2.5 fMRI ...................................................................................................................... 26

3.2.6 Analysis ................................................................................................................. 26

3.3 Results ............................................................................................................................... 30

3.3.1 Pilot Study ............................................................................................................. 31

3.3.2 Cohort Study ......................................................................................................... 32

3.3.2.1 Simulator Data ........................................................................................ 32

3.3.2.2 fMRI Data ............................................................................................... 34

3.3.2.3 Activation Maps and Voxel Counts ....................................................... 37

3.4 Discussion ......................................................................................................................... 38

3.4.1 Pilot Study ............................................................................................................. 40

3.4.2 Cohort Study ......................................................................................................... 41

3.4.2.1 Motion in the Simulator .......................................................................... 41

3.4.2.2 Motion during fMRI ............................................................................... 42

3.4.2.3 Voxel Counts .......................................................................................... 44

4 Conclusions .............................................................................................................................. 45

4.1 Aim 1 ................................................................................................................................ 45

4.2 Aim 2 ................................................................................................................................ 45

4.3 Significance of Work ........................................................................................................ 46

4.4 Future Work ...................................................................................................................... 47

References ..................................................................................................................................... 49

vi

List of Figures

Figure 1: Representative percent signal change across a typical BOLD response. Image modified

from A Primer on MRI and Functional MRI28

. ............................................................................... 6

Figure 2: Representative data during a pilot study with an fMRI simulator: (a) young, (b) elderly,

and (c) stroke subjects. Young subjects exhibit the least head motion. The stroke data exhibited

the largest motion amplitude and were significantly task correlated (10 tasks corresponding to 10

peaks) whereas the elderly data were intermediate in extent between the other two groups. The

majority of motion for both stroke and elderly groups lay in the inferior-superior direction. ....... 9

Figure 3: Illustration of experimental setup and visual-motor task. (a) Diagram of simulator

layout showing a representation of the visual field with real-time motion feedback. (b) Head of

simulator bed with miniBird apparatus and its respective coordinate axes. (c) Visual stimulus

(MRI only) for the gripping task with hand unit pressure bulb shown inset. ............................... 19

Figure 4: Diagram showing inclusion criteria for training eligibility applied to young and elderly

(initially) subjects. ......................................................................................................................... 23

Figure 5: Three event sample of square wave task timing (solid line) and test waveform

representing the task-related fMRI signal (dashed line). The test waveform is obtained by

mathematical convolution of the task waveform and the BOLD hemodynamic response function

(HRF). Timing is representative of a simulator run with 4 s events and 8 s rests. The HRF is

well modeled using a gamma distribution and adds a physiological BOLD response to the task,

such that the test waveform lags the task onset by 6-7 s. ............................................................. 28

Figure 6: Positional head motion data from pilot stroke subjects trained in a unilateral gripping

task with their affected hand. Data plotted in rows for: (a) Subject 1, (b) Subject 2 and (c)

Subject 3. The vertical scale between subjects is not equal. Feedback training substantially

reduced head motion during and after training. Note the major improvement in inferior-superior

motion, where the majority of displacement occurred prior to training. ...................................... 31

Figure 7: Plots for the three subject groups in the simulator are shown for (a) Absolute deviation

(AD) and (b) Cumulative deviation (CD). Error bars represent standard error of the mean. ...... 33

vii

Figure 8: Correlation values (CC) plotted for healthy young subjects in the (a) simulator and

during (b) fMRI. Corresponding behavioural data are given in (c) with respect to the task

performed during fMRI. All error bars represent the standard error of the mean. ...................... 35

Figure 9: Correlation values (CC) plotted for healthy elderly subjects in the (a) simulator and

during (b) fMRI. Corresponding behavioural data are given in (c) with respect to the task

performed during fMRI. All error bars represent the standard error of the mean. ...................... 35

Figure 10: Correlation values (CC) plotted for stroke subjects in the (a) simulator and during (b)

fMRI. Corresponding behavioural data are given in (c) with respect to the task performed during

fMRI. All error bars represent the standard error of the mean. ................................................... 36

Figure 11: Representative brain activity for: (a) young trained, (b) young untrained, (c) elderly

trained, (d) elderly untrained and (e) stroke individual subjects. Note the ipsilateral activity of

the stroke subject with right side paresis. Family-wise error rate was set at P = 0.001 with

nearest neighbour clustering at 20 voxels minimum volume. Colour scale is representative of t-

value. ............................................................................................................................................. 38

viii

List of Tables

Table 1: Summary of runs performed by each subject during time in the simulator and MRI

system. The longer fMRI runs were due to BOLD signal time constraints requiring longer rest

periods. The simulator runs were condensed to minimize time and avoid fatigue of the subjects.

……………………………………………………………………………………………………22

Table 2: Ratings from the subject groups on the difficulty of the two task conditions during

fMRI, where 1 is very easy and 8 is very difficult (mean +/- standard error). ………………….37

ix

List of Abbreviations

fMRI – Functional magnetic resonance imaging

BOLD – Blood oxygen level dependent

DOF – Degrees of freedom

TE – Excitation time

TR – Repetition time

HRF – Hemodynamic response function

EPI – Echo-planar imaging

EMG – Electromyography

CCD – Charge coupled device

1

1 Introduction

1.1 Statement of Research Problem

Head motion has been widely regarded as a source of signal artifact in functional magnetic

resonance imaging (fMRI) that can be very difficult to distinguish from the brain activity signals

of interest1-6

. Random head motion has been shown to decrease the number of activated voxels

detected in brain activation maps (false negative brain activity)6, whereas head motion correlated

with task-related behavior (particularly associated with motor performance)6 during fMRI has

led to false positive brain activity in both simulated and real data acquisition4,5

. The threshold

for acceptable head motion has been shown to be approximately 1 mm, where motion exceeding

this threshold causes a significant increase in image artifact7-9

.

Functional MRI is a widely used neuroimaging tool for the assessment of the natural processes

of aging, as well as neurological disorders such as stroke10,11

. Participants in these groups have

demonstrated higher magnitudes of head motion during visually stimulated motor tasks6,12

. For

example, it is believed that stroke patients have difficulty with head motion during motor tasks

due to the recruitment (co-contraction) of proximal muscles in their attempts to perform tasks

involving more distal muscles6. This is unfortunate, because fMRI has potential to provide new

and important information regarding how individuals recover from stroke, and to inform how

treatments can be developed to improve stroke recovery11

.

It has also been shown6 that stroke participants exhibit significantly more task-correlated head

motion in the inferior-superior direction. Motion in this direction is considered “through-plane”

on a conventional axial (or oblique axial) fMRI scan and results in artifacts that are more

difficult to remove than those produced by motion in the orthogonal directions. Through-plane

2

motion creates disturbances in the steady-state magnetization of tissue and results in signal

changes that are also similar to those of fMRI signals1,13

.

During fMRI acquisition, light restraints (i.e. foam wedges, vacuum pillows, straps, etc.) are

used to help limit head motion. These restraints are most effective in restricting motion in the

medial-lateral direction, and less effective for motion in orthogonal directions. With the desire to

keep patient discomfort and stress at a minimum, head restraint is only lightly used and is not an

extremely effective technique for preventing motion in fMRI.

The current standard method of removing residual artifact due to head motion is through

retrospective image coregistration algorithms1,14

. In this method, the volumetric data set

encompassing the brain, consisting of multiple image “slices” is treated as a rigid body.

Coregistration is the process by which each volumetric data set in the fMRI time series is aligned

in relation to the data acquired at a specific time point, also providing estimated head motion

parameters. Interpolation is performed to correct voxel intensities as the slices are realigned15

.

Coregistration is able to correct sub-millimeter motions in a time series but a dependence on

image quality as well as interpolation artifact limit this technique in the removal of artifact

caused by large motions16

. Interpolation results in a loss of image quality (blurring) and leaves

spin-history artifact uncorrected15

. Improved head motion correction would be useful.

Alternatively, there are several methods to correct for head motion prospectively in an attempt to

address large motions and spin history artifact, but they are not without faults. Examples of such

methods are Prospective Acquisition Correction (PACE)15

, navigator echoes17,18

, and external

monitoring19,20

. Currently, however, these methods either lack temporal resolution, or they

introduce new, costly hardware that can interfere with the scanning process.

3

The fMRI simulator, a mock-up of a real MRI system configured for fMRI experiments,

provides a new avenue of research with respect to motion correction. The fMRI simulator can be

used as an alternative environment to train subjects to improve their ability to keep their head

still. To date, there have been very few studies exploring the use of such a simulator to reduce

head motion. These studies have focused on using a simulator to optimize fMRI task design21

or

to study the effects of anxiety22-24

when subjects (mainly children) performed cognitive tasks in

an MRI environment. The fMRI simulator has been shown to be effective in creating a realistic,

claustrophobic MRI-like environment, causing an increase in anxiety and decrease in task

performance compared to performance undertaken in an unrestrictive environment23,24

. Another

study concluded that there was no substantial difference between motion in the simulator and a

real scanner6. Also noted was the decrease in head motion when a stroke subject was allowed to

practice a task in the simulator. From these few studies, it can be seen that the fMRI simulator

has potential as a training tool for subjects to reduce their head motion prior to scanning.

However, further study would be useful to strengthen this assertion.

1.2 Specific Aims

This thesis investigates the advantages of using an fMRI simulator to train subjects to limit their

head motion prior to real scanning. A method for closed loop visual feedback training to reduce

head motion is described, and then explored by testing of young, elderly, and stroke populations

in the fMRI simulator and during real fMRI experiments.

Aim 1: Develop an fMRI simulator environment and training protocol for subjects to learn to

reduce their head motion while performing an auditory-motor task.

1a.) Characterize and validate motion tracking hardware in the simulator and design a closed

loop visual feedback interface.

4

1b.) Create a task protocol in the simulator that allows subjects to be evaluated before, during

and after training.

Aim 2: Evaluate the effect of training on subjects in the simulator and subsequent fMRI.

2a.) Identify parameters that appropriately characterize the motion of the subjects, particularly

those causal to the associated artifact in fMRI images (i.e. motion in the inferior-superior

direction and/or correlated with the task). Analyze head motions observed in the

simulator and in fMRI examinations, for healthy young and old subjects, as well as a

small number of example stroke patients in the chronic phase of recovery from stroke.

2b.) Analyze acquired fMR images and investigate the effect of simulator training on maps of

brain activity.

5

2 Background

2.1 Functional MRI and the BOLD Effect

The most established fMRI technique relies on the blood oxygen level dependent (BOLD) signal.

The BOLD signal is measured by monitoring fluctuations in the T2* relaxation decay curve25,26

.

During performance of a behavioural task, brain regions activated beyond their basal levels

experience an increase in oxygenated hemoglobin (oxyhemoglobin) through elevated blood flow

in the surrounding microvasculature27

. Oxyhemoglobin is diamagnetic (no unpaired electrons

that are attracted to a magnetic field), whereas deoxyhemoglobin is paramagnetic (unpaired

electrons present and attracted to a magnetic field). The transverse relaxation time, T2*, an

imaging constant that describes the decay of MR signals, is sensitive to the magnetic properties

of biological tissues, and thus the relative fractions of oxy- and deoxy-hemoglobin. Higher

concentrations of deoxyhemoglobin cause a slightly more rapid decay of the MR signal (smaller

T2* value), compared to the opposite situation of higher concentrations of oxyhemoglobin,

which cause slightly slower decay of the MR signal (larger T2* values). Functional MRI uses

gradient- echo pulse sequence techniques to maximize the contrast from changes in the T2*

decay curve that occur during brain activity, by sampling the decay at the optimal echo time

(TE). For example, this TE value is approximately 30 ms for fMRI conducted at a magnetic

field strength of 3.0 Tesla.

The primary physiological effect that is exploited in BOLD contrast is the relatively large

hyperemic increase in blood flow (increased oxygenation) that occurs in response to the transient

increase in metabolism (decreased oxygenation) of activated neurons. Putting these events in

chronological order, a brief burst of neural activity is associated with an initial drop in BOLD

signal (1-2s), followed by a large peak (6-10s), then a drop back to negative signal amplitude

6

with a slow return to baseline (8-20s)28

(Figure 1). These temporal characteristics have been

modeled after a gamma probability density function and are collectively called the

haemodynamic response function (HRF)29

.

Figure 1: Representative percent signal change across a typical BOLD response. Image

modified from A Primer on MRI and Functional MRI28

.

2.2 Motion Artifact in Functional MRI

Functional MRI is used in tandem with conventional MRI to study brain function. The benefit is

that high contrast anatomical MRI scans can be overlaid with images (typically shown in colour)

calculated from scans sensitive to the BOLD effect, providing activation maps. Brain activation

maps are usually calculated in relation to various behavioural tasks (motor, visual, auditory, or a

combination thereof), participant groups (young, old, male, female, etc.) and other covariates

(heart rate, handedness, etc.) to help explain brain function in neuroscience and neurological

studies30

. To study these effects, a popular technique in fMRI consists of a task performed

7

repeatedly between rest intervals, called event-related task design. Each event and subsequent

rest period allows some time for the BOLD signal to decay, with multiple events (typically a

dozen or more) instituted for signal averaging purposes. This results in a relatively long scan

where the subject must remain vigilant to the task and intermittently active. Lengthy scans and

the confining environment of the MRI system make potential for head motion an ever-present

problem in fMRI.

Primary motion effects, due to bulk movement of the head while scanning, can cause blurring,

banding and streaking in images and can decrease spatial resolution16,31

. Echo-planar imaging

(EPI), a rapid acquisition technique used extensively in fMRI, is used primarily to provide

„snapshot‟ images that effectively freeze motion such that the above effects provide a minimal

contribution of signal artifact. However, EPI data remain affected by motion-induced signal

fluctuations between adjacent voxels during the time series data collection. Changes in tissue

composition and contrast within voxels during a „single-shot‟ (entire slice data acquired from

one excitation) acquisition are the cause of these signal fluctuations. This is known as the

„partial voluming effect‟ and can cause signal changes of the same magnitude as the BOLD

response. Furthermore, partial voluming is of particular concern in regions where there are voxel

composition changes from brain tissue to either bone or air (sinuses), which contribute zero

fMRI signal. Task correlated motion can compound the problem because when these signal

changes coincide with expected neuronal events, they introduce false positive brain activity.

Other random or non-correlated motions lead to an overall increase in variance and loss in

detection sensitivity, introducing false negative brain activity.

There are also indirect effects of motion during fMRI. One frequent problem is the

magnetization history artifact, also termed „spin history effect‟, that occurs when there are large

8

motions (> 1 mm) through the scan plane of image slices, which is normally fixed throughout the

entire scan. For example, in the case of axial (transverse) slices, the through-plane direction is

the superior-inferior axis (in and out of the magnet bore). For every scan repetition, there are a

number of 2D slices acquired to make up a 3D volume. If motion occurs in the inferior-superior

direction during a scan, it could cause tissue from one slice to move into that of another,

resulting in a transient effect on magnetization. This will affect the signal acquired in subsequent

scans because the affected tissue may not be at steady state when excitation occurs, a problem

outlined thoroughly by Friston et al.1. Extending the scan repetition time (TR) can minimize the

effect of spin history by allowing more time for tissue to reach steady state. However, doing this

will extend an already lengthy scan and reduce sampling, which are both undesirable. Single

slice EPI is most susceptible to through-plane motion13

. In a multi-slice acquisition with EPI,

spin history artifact is secondary to the partial voluming effect, but still cannot be accounted for

by rigid-body motion correction.

2.2.1 Subject Motion

Long scan times and performance of fMRI tasks make subject movement a particular problem in

fMRI studies. This is of particular concern for motor tasks where the subject is required to move

precisely or to control an instrument physically while attempting to keep their head still for

scanning. Head motion is highly variable across subjects, and is roughly a function of age and

neurological medical history. Seto et al. have thoroughly characterized head motion in stroke,

elderly and young subject populations6. While performing motor tasks of the hand and foot, they

found that the severity of head motion from worst to best was: stroke (highly task correlated),

elderly (moderately task correlated and also random) and young (rarely task correlated, with a

random component well tolerated in typical fMRI data processing). It was found that the

9

majority of the motion exhibited by the elderly and the stroke subjects was in the inferior-

superior direction. In 2009, these results were reproduced in a pilot study by Ranieri et al.

(Figure 2)32

. It was shown that elderly and particularly stroke subjects exhibited unacceptable

levels of motion in regard to fMRI data acquisition. The representative head motion data shown

for the stroke subject are indicative of an unusable scan. The highly task correlated head motion

data for stroke subjects can be attributed to the decrease in fine control of their affected hand and

the recruitment of proximal muscles, causing motion of the upper limb and head6. Also worth

noting, an extensive study regarding subject anxiety during MRI was done by Szameitat et al.

who showed that stroke subjects reported higher levels of discomfort and anxiety than healthy

controls in the same imaging battery33

.

Figure 2: Representative data during a pilot study with an fMRI simulator: (a) young, (b)

elderly, and (c) stroke subjects. Young subjects exhibit the least head motion. The stroke data

exhibited the largest motion amplitude and were significantly task correlated (10 tasks

corresponding to 10 peaks) whereas the elderly data were intermediate in extent between the

other two groups. The majority of motion for both stroke and elderly groups lay in the inferior-

superior direction.

10

2.3 fMRI Simulator

In the literature, an fMRI simulator is a slightly ambiguous concept. One definition refers to a

computational algorithm used to test experimental methods (pulse sequences, image

reconstruction, data analysis, etc.) without the use of a scanner34,35

. The use of a bore replica to

give adult participants a realistic simulation of the MRI scanning environment is a relatively new

idea and is the definition pertinent to this thesis6. Such simulators have been used to help

children become more comfortable with the scanner so as to eliminate the need for sedation22

.

The fMRI simulator is made to look, sound and feel like being in the real scanner. A simulator

also enables the use of electronics (eg. tracking systems, heart rate monitors) and tools that are

not necessarily “MR-safe” for use in the high magnetic fields present in MRI systems. This can

substantially reduce the cost of equipping a simulator because MRI compatible tools and devices

are normally medical grade and/or specially designed without mass production for neuroimaging

niche markets. Use of such electronic devices, which may not be easily obtainable commercially

in MR-safe versions, may provide the capability to improve upon characterization of behavior or

physiological measurement in comparison to what can be achieved during real fMRI

experiments. Thus, the simulator can be used to mimic an MRI environment so that participants

can be familiarized with a task and trained, or tasks can be prototyped prior to scanning. This

makes the simulator an ideal environment for technical development. For example, MacIntosh et

al. demonstrated that an MRI simulator provided an adequate environment for the optimization

of a motor ankle dorsiflexion task21

. Simulators have also been used in the study of anxiety and

the corresponding effect on cognition that occurs when subjects are asked to perform tasks in an

MRI environment23,24

. One group demonstrated the benefit of using a simulator to condition

children to the stresses of the uncomfortable, claustrophobic MRI environment prior to

11

anatomical scanning, in order to eliminate the need for sedation22

. It has also been demonstrated

that practice in a simulator environment, combined with some coaching, reduced head motion in

a stroke patient performing a motor task, and that the head motion exhibited in a simulator is

similar in character to that observed in actual fMRI studies6. From this, simulators can provide a

realistic environment for various training techniques to be employed with the aim of reducing

head motion. Given the role that simulators can potentially play in reducing motion artifacts in

fMRI experiments, more research investigating specific simulator applications would be useful.

2.3.1 Training

A question of central interest in this thesis is whether an fMRI simulator, equipped with a closed-

loop visual feedback interface, can be used as a training tool to help subjects decrease their head

motion during motor tasks. Based on the fundamentals of closed-loop feedback and motor

learning laid out by Adams (1971)36

, many studies outside the fMRI arena have focused on using

visual feedback to assist in training exercises. Adams suggested that knowledge of results while

performing a task can increase the effectiveness of motor learning. It has been shown that visual

feedback of task results in real time can help elderly subjects rehabilitate posture and balance37-

39. In these studies, subjects were instructed to perform gait tasks on a force plate while provided

with visual feedback with respect to their centre of gravity. Marked improvement was shown in

subjects trained with visual feedback over those trained without it37

. Visual feedback has also

been effective in electromyography (EMG), where subjects exhibiting facial nerve paresis

(partial paralysis) were fed back their EMG data in real-time in an attempt at neuromuscular

rehabilitation40

. Subjects trained with visual and auditory feedback showed improvement in

accuracy for breath timing in respiratory-gaited radiotherapy41

.

12

Training in an fMRI simulator potential could be used as a supplementary tool to reduce head

motion in subject populations where it is a problem. Added to current fMRI methods, simulator

training potentially could help remove head motion before it becomes a problem in scanning.

When substantial motion occurs during fMRI, especially motion that is task correlated or

through-plane, the associated signal artifacts are difficult to remove. A review of current

pertinent motion correction methods for fMRI, and their limitations, is given below as a prelude

detailed consideration of fMRI simulator experiments.

2.4 Limitations of Current Methods

2.4.1 Physical Restraint

The use of restraints to limit head and body motion are effective but at the cost of subject

comfort and increased anxiety. For imaging brain function, it is pertinent to make the subject as

comfortable as possible given the already confining MRI environment, and the length of the

imaging session (typically one hour in the magnet bore). The introduction of restraint could

affect behaviour of the subject. Also, heavy restraints may promote readjustments by the subject

to relieve discomfort at the pressure points, exacerbating the problem16

. Only light restraints in

the form of spongy cushions or vacuum pillows are used to limit mobility of the head as a

consequence. These cushions limit motion primarily in the medial-lateral direction and are less

effective at limiting inferior-superior motion. Other restraints, such as straps applied to the limbs

of a subject, have been shown to be ineffective at limiting head motion6. A bite bar, which

usually consists of a moulded mouth piece fixed to the head coil or scanner bed, is another form

of physical restraint that has been used in the literature42

. A bite bar is a very effective tool to

limit rigid body motion of the head but poses a problem with some patient populations,

especially those suffering with dysphagia (problems swallowing). Many stroke patients suffer

13

from this condition and would not tolerate the presence of a mouth piece. Because of this, bite

bars are most applicable to young, healthy subjects. There remains a need to develop a way to

reduce or counteract head motion without the use of physical intervention.

2.4.2 Retrospective Coregistration

Motion during fMRI is primarily dealt with retrospectively, in data “pre-processing” prior to the

calculation of brain activation maps. It is common for fMRI analysis packages to include some

form of retrospective correction technique to minimize the effect of motion on fMRI time series

data43,44

. For example, analysis packages (i.e. AFNI, Brain Voyager, SPM2, etc.) use image

coregistration to realign the imaging volume to a reference using rigid body estimates of motion

from voxel intensities. The resultant motion estimates can be used further as nuisance regressors

in the general linear model (GLM) calculation of voxel activations44

. While these techniques are

effective at removing the effects of small displacements, large motions often require manual

intervention from the user to remove the problematic slices and rerun the coregistration

algorithm. Coregistration employs at least some form of interpolation of voxel intensities, which

can cause an undesired smoothing effect on the data. Image coregistration also assumes rigid

body motion. Changing voxel intensities from brain activation, for example, can bias motion

estimates under the rigid body assumption and result in falsely corrected data45

. Furthermore,

secondary effects of motion, such as spin history caused by through-plane motion, cannot be

corrected retrospectively by coregistration because detailed information on spatial variation of

relaxation times is required15

. Head position tracking by external monitoring systems can be

used to realign fMRI data46; however, this approach still assumes rigid-body head motion and

does not eliminate blurring from image interpolation. Although the improvements provided by

motion correction algorithms are indispensible, the remaining residual errors and incapability to

14

correct for large motions have motivated research to develop other methods that aim to attenuate

motion artifacts as they occur.

2.4.3 Navigator Echoes

Navigator echoes have been used as a method of prospectively and retrospectively correcting for

head motion18,17. In the simplest implementation, navigator echoes constitute additional MRI

data acquisitions such as projections of the head in three orthogonal directions, from which time-

dependent translations and rotations can be estimated. Although of some use in anatomical

imaging, motion estimates from this technique are dependent on the positional accuracy

achievable by MRI measurements. In the fMRI context, this technique has inadequate accuracy

and can have misregistrations of up to half a millimeter15,47. Furthermore, this method suffers

from a relatively low temporal resolution (10 Hz) and requires additional scans to estimate

movement.

2.4.4 PACE

An alternative to navigator echoes is the Prospective Acquisition Correction (PACE) method15

.

The PACE method identifies motion through image coregistration of two previously scanned

slices and applies the estimated motion parameters to the next scan in real-time. This method

has also been shown to remove the effects of slow motions successfully in fMRI data, although

the repetition time TR between fMRI acquisitions of the brain volume is typically 2 s. Head

motion could vary considerably during this time, leading to residual motion artifacts.

2.4.5 External Monitoring

External tracking, such as achievable by optical methods, has been investigated to address the

spatial and temporal errors associated with previous prospective and retrospective correction

techniques. Retrospective motion correction by external monitoring using optical tracking and

15

skin mounted tracking tools can reduce motion correlated voxels for up to 6 mm of motion46.

Furthermore, prospective motion correction using external monitoring has been demonstrated as

an effective means of reducing motion artifact including spin-history19,20

. These benefits are

achievable optically through use of stereoscopically-arranged infrared (IR) sensitive charge

coupled device (CCD) cameras that record the motion of illuminated IR-reflective markers.

Accuracy in the 10 to 100 micron range is achievable with fast sampling rates (60 Hz)20

.

However, the markers are affixed to the scalp, which can move in relation to the skull and may

lead to inaccuracy when correcting for brain position. The cost of MRI-safe peripheral

equipment as well as the challenge of implementing and calibrating such equipment in the

confines of an MRI system, also have limited the widespread use of this technique.

The motion correction methods described above have utility, but do not provide a complete

solution to the difficult problem of head motion effects on fMRI data. Retrospective

coregistration is ineffective in addressing large motions and spin history effects through-plane.

The main prospective techniques described above address this issue best when external

monitoring is adopted. However, external monitoring requires the purchase of expensive MR

safe peripheral hardware such as cameras, lights, and shielded cables.

The challenges associated with removing residual motion artifact during or after fMRI data

collection give renewed impetus to develop and investigate a method of reducing motion before

it can occur. This thesis investigates the efficacy of using visual feedback training in an MRI

simulator to improve head motion prior to actual scanning. Although simulators have been used

to assess head motion6, the specific method of employing real-time feedback training has not

been investigated in the past. It presents an inexpensive method of reducing motion directly at

16

the source of the problem – the subject - to help limit the amount of motion artifact that must be

corrected in post processing.

17

3 Development of Simulator Training to Reduce Head Motion Artifact in fMRI

3.1 Introduction

As indicated in previous chapters, the fMRI simulator has potential as a tool to reduce head

motion in subjects prior to scanning. The use of simulators is worth exploring, given that other

methods for motion correction of fMRI data do not completely solve the problem. There is little

existing literature on the use of simulators, and no quantitative work has been done previously to

evaluate the efficacy of an fMRI simulator as a motion prevention technique. This thesis

explores the effect of visual feedback training on head motion in young, elderly and stroke

subjects. An fMRI simulator was developed to provide an MRI-like environment where subjects

could be evaluated with respect to head motion before, during and after feedback training. The

effect of training was characterized using calculated metrics that appropriately described

problematic head motion with respect to fMRI data corruption. The methods and results from

this study are outlined and discussed in detail below.

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3.2 Methods

The thesis research was conducted in several phases. First, fMRI simulator methodology was

developed and validated. Part of the validation included an initial pilot study with stroke

subjects, to confirm previous anecdotal reports of simulator training benefit. The outcome of the

development and validation phase was a training protocol focusing on head motion measured

during four shortened fMRI-like task runs with motion feedback training. Subsequently, cohort

studies were performed in which multiple subject groups were evaluated to investigate the effect

of simulator training: young and healthy adults, and patients in the chronic phase of recovery

from stroke. Functional MRI was also conducted after simulator training to investigate whether

beneficial training effects persisted. The methodological details of each phase are given below.

3.2.1 Simulator Hardware

The fMRI simulator (Fig. 3) used was a commercially available mockup including an MRI

patient table, head coil, bore shell with audio speakers, and magnet cover (Psychology Software

Tools, Pittsburgh, PA). Upgrades were made in-house to enhance the realism of the system. A

discarded, previous face plate of an actual MRI system (Magnetom Trio, Siemens) was fixed to a

wooden frame and positioned over the front of the simulator bore. Figure 3b shows a zoomed

view of the simulator bore area, with Fig. 3a showing a block diagram of all system components.

A recent adaptor on the replica head coil allowed for the use of an actual Siemens head coil

mirror for viewing visual stimulus presentations (Fig. 3b). Viewing also required a projector

(NEC VT695) positioned behind the simulator to illuminate a back-projection screen affixed to

the rear bore opening (Fig. 3a). Headphones (Sony) were used to deliver auditory stimuli to

subjects while they performed behavioural tasks, and to deliver sound recordings of scanner

noise throughout data collection.

19

Figure 3: Illustration of experimental setup and visual-motor task. (a) Diagram of simulator

layout showing a representation of the visual field with real-time motion feedback. (b) Head of

simulator bed with miniBird apparatus and its respective coordinate axes. (c) Visual stimulus

(MRI only) for the gripping task with hand unit pressure bulb shown inset.

A commercially available position tracking system was used in the fMRI simulator to measure

head motion. The miniBird (910007-A, Ascension Technology Corporation, Vermont) is a

position tracking system capable of tracking in 6 degrees of freedom (DOF). The tracker uses a

pulsed DC magnetic dipole generated from a transmitter which allows the receiver to obtain

20

reference position data based on induced voltages from sensors configured in three orthogonal

directions. The tracking system relies on a weak magnetic field, has vendor-provided accuracy

specifications for movements within a sensitive volume much larger than intended in this study,

and was operated within range of many metallic and electronic sources of interference.

Consequently, detailed characterization of miniBird system performance was required.

A high precision micro-positioning stage (Elliot-Martock Design, Hertfordshire) was used to

characterize the miniBird system. The entire apparatus was placed on the simulator bed where

the subject‟s head would rest, and slightly inserted into the bore as far as would allow for

adjustment of the stage between measurements. Spatial drift was measured by sampling tracking

data from a static object (receiver on stage) over 90 min. Translation accuracy was measured by

incrementally moving the receiver through an 8 mm x 8 mm x 8 mm space, with measurements

every 2 mm, bringing 5 measurements per axis for a total of 125 measurements. Each

measurement was recorded over 10 s at 60 Hz. It was determined that the accuracy and

resolution for translation were acceptable for this study at < 0.2 mm. Drift due to thermal effects

reached steady state after 15 min. Thus, the system was powered on 30 min. prior to tracking

human subjects.

3.2.2 Task Protocol

Subjects were guided through a series of task runs in the simulator and in the MRI. All subjects

were given a brief description of the study but were not informed of any hypotheses prior to

completing the tasks.

In the MRI simulator, subjects were instructed to perform an auditory-motor task in simulation

of the visuo-motor task that they were to undergo afterwards during fMRI (c). The task

consisted of unilateral hand gripping of a pneumatic bulb to move a cursor into a target over a 4 s

21

event window. When the subject squeezed the bulb, the cursor displaced vertically in real-time

and could be maintained in the target area with application of the appropriate pressure. The

pressure bulbs were re-purposed from a sphygmomanometer and attached to pneumatic tubing

that lead to a receiver box with a piezoelectric sensor, custom built in-house. Output from the

sensor was sent to a data acquisition receiver (DAQ) (NI, Austin, TX) and relayed to a stimulus

computer (Intel Core2 Duo, 3GB RAM) running Labview 7 (NI, Austin, TX), which was

connected to an LCD projector and headphones for presenting visual stimuli back to the subject

(Fig. 3a).

In the MRI system, the task was visually cued by the appearance of the target. Because the

subjects were presented with visual feedback of their head motion in the simulator, it was

determined that presenting them with a visual target stimulus simultaneously with head motion

data would cause divided attention effects. This was of particular concern for stroke subjects

who potentially could suffer from attention deficits. Therefore, the task was simplified by

changing the queue to an auditory tone and removing the visual stimulus. Subjects were

instructed to grip the pressure bulb closed during the audio tones to match a worst case target

event (i.e. a high positioned, narrow bar). The visual display was instead occupied by real-time

head motion feedback driven by the miniBird tracking system (Fig. 3a).

Subjects were instructed to change into gowns so they could move directly from the simulator to

the MRI system after training. The subjects were given head phones and the volume of the

auditory queue was adjusted so that they could hear it comfortably. The miniBird tracking

sensor was fixed to the topmost part of the subject‟s forehead with medical tape; this technique

minimized unwanted skin shifts (skin artifact) from blinking and brow movements. The replica

head coil and bore mirror were placed over the head and the subject was moved into the

22

simulator bore. No side cushions or other forms of motion restraint were used to ensure a worst

case during training, that being uninhibited head movement. Once in position deep in the

simulator bore, the subjects were given the pressure bulb in their dominant (or affected) hand to

start the task runs.

In the simulator, subjects were instructed to keep their head still and squeeze the bulb closed for

the duration of the audio tones and release during the rest periods. They underwent four runs

(Table 1). The first run, no feedback (auditory stimulus only), was used to do an initial

assessment of head motion. The resulting data were subsequently analyzed in relation to the

flow chart shown in Figure 4, which outlines criteria for simulator training. These criteria were

developed from initial pilot data (see below) according to the principle that individuals

exhibiting minimal head motion should not be trained. The criteria included thresholds on

maximum movement from baseline and task-correlated motion (see below for description of

quantitative metrics), above which motion was judged to be sufficiently problematic that training

was warranted.

Table 1: A summary of runs performed by each subject during time in the simulator and MRI

system is shown. The longer fMRI runs were due to BOLD signal time constraints requiring

longer rest periods. The simulator runs were condensed to minimize time and avoid fatigue of

the subjects.

Environment Run Description Feedback Stimuli Duration Timing

1 Pre Training None

2 Training1 Motion

3 Training2 Motion

4 Post Training None

1 Easy Condition Task

2 Hard Condition Task

130 seconds

310 seconds

10s delay, 4s event, 8s rest,

10 events

10s delay, 4s event, 14-18s

rest (jittered), 15 eventsMRI

Simulator Auditory

Visual

23

Visual motion feedback was introduced in the second and third runs. The subjects were first

presented with the visual feedback interface and instructed to move their head to demonstrate the

associated signal changes on the display: sagittal inferior-superior nodding, axial medial-lateral

rotation, and axial anterior-posterior displacement (Fig. 3a). After this introduction, subjects

started the second run under instruction to try and keep the lines between the translucent red bars

overlaid on the motion display. These thresholds represented approximately 1 mm of motion in

any of the three axes, a threshold determined to be the point at which motion causes BOLD

artifact7. The task was repeated for run 3. The fourth and final run was conducted in the same

manner as the first run to enable quantitative comparison of head motion before and after

feedback training.

Figure 4: Diagram showing inclusion criteria for training eligibility applied to young and elderly

(initially) subjects.

24

After training, subjects participated immediately in an fMRI exam. Setup for fMRI was the

same as in the simulator, except that additional padding was laid under the knees for comfort and

side cushions were positioned inside the head coil to help limit head motion. Task sequences

differed by length and rest timing (Table 1) and were longer during the fMRI exam than during

the simulator training due to the sluggish nature of the BOLD hemodynamic response. The task

events were cued visually with the target and cursor display (Fig. 3c), and a visual fixation point

(red dot) was shown during the rest periods. For each event, the target was placed in a different

vertical position with an equal distribution of low to high positioning over the course of the run.

The task was performed with the same hand as in the simulator. The subjects were presented

with two difficulties: easy (wide target) and hard (narrow target). Due to the low difficulty of the

task, designed for stroke and healthy elderly, the healthy young subjects performed the narrow

target condition twice with the second run adjusted for increased difficulty by varying pressure

bulb gain between events. Accuracy and response time were recorded during fMRI to

characterize gripping behaviour.

All subjects that participated in the experiments were recruited with the approval of the Research

Ethics Board at Baycrest. Subjects gave their free and informed consent to participate in

accordance with the principles of the Declaration of Helsinki as developed by the World Medical

Association. All healthy subjects were free from neurological or psychiatric impairments.

Stroke patient volunteers were recruited with the assistance of Dr. Jon Ween, Director of the

Stroke and Cognition Clinic at Baycrest. These patients were free from all other neurological

and psychiatric impairments other than stroke.

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3.2.3 Simulator Pilot Study

An initial validation of the simulator and training protocol was performed with a pilot group of

stroke subjects (3 subjects: all male, age range 39–72 years). All subjects were in the chronic

phase of recovering from a stroke and exhibited mild to moderate motor impairment of the hand

characterized by a Chedoke-McMaster score between 3 and 5 out of 7. The Chedoke-McMaster

Stroke Assessment has been validated as both a clinical and research tool for discriminating

between stroke patients based on motor impairment48

. The scale scores complete paralysis as 1

and perfect motor function as 7. The pilot study was done using a preliminary version of the

training protocol that had visual task timing (instead of auditory task timing) and a basic

feedback interface, similar, but not identical to that shown in Fig. 3a.

3.2.4 Cohort Study

Three different groups of subjects were investigated under conditions of simulator training

(“trained”) and no-simulator training (“untrained”) with their performance studied in subsequent

fMRI. Ten healthy young subjects (6 male; 4 female; age range: 18-25 years), were divided into

two equal groups (trained: 2 male, 3 female; untrained: 4 male, 1 female). The sex balance

between groups was uneven because the selection criteria were based on head motion

performance according to the flow chart shown in Fig. 4. Eleven healthy elderly subjects (age

range: 60-80 years), were divided into two groups (n=7 trained: 2 male, 5 female; n=4 untrained:

1 male, 3 female) for trained and untrained conditions. During recruitment, the initial intention

was to recruit elderly subjects in each group according to motion criteria (Fig. 4), as undertaken

for the young group. However, it became clear during experimentation that it was very

challenging to find healthy elderly subjects that did not require simulator training on this basis.

Therefore, the decision was made to recruit healthy elderly subjects for the untrained group

26

irrespective of their head motion characteristics. Due to practical limitations on the time

available for subject testing, this resulted in a slight imbalance between the trained and untrained

groups in terms of sample size and sex.

Five stroke subjects (4 male, 1 female; age range: 35 – 80 years) were initially selected to

participate in the study. Inclusion criteria included a Chedoke-McMaster Stroke Assessment

between 3 and 5. Between recruitment and the actual testing session, one patient improved

markedly in motor function and was excluded. Another patient was excluded on the basis of

substantial cognitive impairment that precluded compliance with the motor task. Thus, three

subjects, all male, were chosen for subsequent analysis. Given that stroke patients were expected

to exhibit the greatest head motion of the three groups, the small sample size of patients

obtained, and the logistical difficulty in timely recruitment of additional stroke patients, the

decision was made that all three individuals were submitted to fMRI simulator training. No

stroke patients were allotted to an untrained group.

3.2.5 fMRI

All imaging was conducted using the research-dedicated 3T MRI system at Baycrest (TIM Trio,

Siemens Medical Solutions Inc., vb15 software) with a 12-channel head coil. High resolution

anatomical scans were taken with a T1-weighted 3-dimensional rapid gradient echo (MP RAGE)

sequence with 2000 ms TR, 2.63 ms TE, 256 mm field of view, and 160 slices. Functional MRI

BOLD acquisitions during task performance were acquired with a 2D multi-slice EPI sequence

having 2000 ms TR; 30 ms TE, 70 degree flip angle, 28 slices, and 155 time points.

3.2.6 Analysis

All motion data were batch processed using scripts written in MATLAB R2008 (The Mathworks

Inc., Natick, Massachusetts). Motion data in the form of Cartesian coordinates were taken from

27

each task run performed in the simulator and from each run during actual imaging as estimated

from fMRI pre-processing. The data were initially reduced into a quadrature sum representing

the vector magnitude of motion. This resulted in a 1D, positive definite time course capturing

the majority of the variance from each motion axis. Vector magnitude is given by:

√ , [1]

where x, y and z are displacements for each coordinate axis. From this time course, several

motion parameters were calculated after normalizing the data by subtracting the first sample in

the series from the following n samples. Each measurement in the time course was then relative

to the initial position of the subject. Absolute deviation (AD) was calculated as the mean

displacement from initial position across each point in the data.

∑

, [2]

where n is the number of samples the motion data. In order to quantify task-correlated motion,

the correlation coefficient (CC) was calculated to represent potential covariance between the

vector motion and the task-related fMRI signal. For this calculation, a task waveform was

generated that represented the task timing for the run. This was done using a square waveform

with ones and zeros, ones representing each task event in the array. The task waveform was

convolved with a hemodynamic response function (HRF)29

with the resulting task-related fMRI

waveform, or “test waveform” exhibiting a shape characteristic of BOLD event-related

responses. An example test waveform is shown in Fig. 5. Subsequently, CC was calculated

according to:

∑ ̅ ̅

, [3]

28

where t is the test waveform, ̅ and ̅ are sample means, and and are the sample standard

deviations of the vector magnitude of motion and the test waveform, respectively.

Figure 5: Three event sample of square wave task timing (solid line) and test waveform

representing the task-related fMRI signal (dashed line). The test waveform is obtained by

mathematical convolution of the task waveform and the BOLD hemodynamic response function

(HRF). Timing is representative of a simulator run with 4 s events and 8 s rests. The HRF is

well modeled using a gamma distribution and adds a physiological BOLD response to the task,

such that the test waveform lags the task onset by 6-7 s. Motion artifacts that covary with the

test waveform will contribute to false positive brain activity.

Cumulative point-to-point motion (CD) was calculated as the sum absolute value of the first

difference of the motion data. This metric characterizes the net incremental motion as a

supplement to the AD value, which is a measure of central tendency:

∑ | | [4]

These three parameters encompassed the key independent performance characteristics of the

motion data for analysis.

29

Analysis of the fMRI data was done using Analysis of Functional Neuroimages (AFNI), version

2008_07_1849

. The first 10 time points (20 sec) for each run were discarded to eliminate

possible head motion caused by the onset of the scan and to discard the initial signal decay

associated with the fMRI signal baseline reaching steady state. The remaining time series data

were subsequently preprocessed to suppress potential artifacts arising from cardiac and

respiratory motion and were coregistered to the first remaining time sample to correct for rigid-

body head motion. Following this step, the two runs collected for each subject were spatially

smoothed using a 5 mm full width at half maximum Gaussian filter, normalized by the run-wise

mean within each voxel and concatenated together to form a single dataset. General linear

modeling was then performed voxelwise with a square wave model for each task and fourth

order Legendre polynomial detrending. This yielded a colour map of brain activation (percent

fMRI signal from baseline) for each task and for each subject. For task activation, areas were

colour coded according to t values that were statistically significant after correction for multiple

comparisons with nearest neighbour clustering (minimum volume of 20 voxels) and family-wise

error rate of p = 0.001. Activated voxels were reported for the young and old groups for

comparison between trained and untrained conditions. The same calculation was performed but

with the square wave task model replaced with motion estimates (described below) as the

primary regressors, allowing for activated voxels associated with motion to be measured.

During reconstruction of the fMRI data, the software used an iterative weighted least squares

approximation to realign the time series data to correct for motion. The output contained motion

estimates in 6 DOF for each TR (2 s, 0.5 Hz) compared with the initial slice position. These data

were used to measure the head motion of subjects in the fMRI scanner and were analyzed in an

30

identical fashion to the simulator data from equations [1] to [4]. AFNI has been regarded in the

literature as an accurate and reliable means of studying motion in fMRI50,51,43,44

.

Behavioural data was recorded during fMRI by the stimulus computer running Labview. The

performance metric used was the time, in seconds, that the cursor spent within the target area

during each event. Subjects were asked to perform the task (make the cursor reach the target) as

quickly as possible when the target appeared. Therefore, response time was stressed during the

task. Variability in the „time in target‟ measurement captured both response time and accuracy.

These data were averaged across all events for each run. Qualitative data was taken where the

subjects were asked to rate the difficulty of the task after each run on a scale from 1 (very easy)

to 8 (very difficult).

The three motion parameters and the calculated voxels were analyzed for group, condition and

training (trained subjects in simulator only) effects in a one way univariate analysis of variance

(ANOVA) as well as a three way repeated measures ANOVA. Post-hoc pairwise tests were

performed using a Bonferroni correction for multiple comparisons. The statistical analysis was

performed using Statistical Package for the Social Sciences (SPSS) (IBM, New York). The

small sample size of the stroke group, due to practical constraints with recruitment, was not

amenable to ANOVA calculations and thus the results for stroke patients are reported more

anecdotally.

3.3 Results

During the tasks in the simulator and the scanner, all subjects performed the task correctly. That

is, an attempt to grip and hold the cursor in the target zone was made corresponding to each

event stimulus.

31

3.3.1 Pilot Study

Individual parametric analysis for the pilot stroke group showed a substantial decrease in total

and task correlated motion after training in the simulator. All of the subjects were able to

decrease their head motion during and after the training runs. It was noted that subjects usually

performed worse in the first training run as they learned the motion feedback interface.

Improvement was observable in the motion range values for each participant post-training,

compared to pre-training: (1) 11.25 mm before, 0.83 mm after; (2) 1.63 mm before, 0.67 mm

after; (3) 4.47 mm before, 0.51 mm after. Similar effects were observed in the CC values (P <

0.05): (1) 0.37 before, 0.27 after; (2) 0.30 before, 0.24 after; (3) 0.43 before, 0.15 after. One

particular subject (age 72, Chedoke score of 5) was able to limit his head motion to below 1 mm

after training, with initial motion exceeding 10 mm.

Figure 6: Positional head motion data from pilot stroke subjects trained in a unilateral gripping

task with their affected hand. Data plotted in rows for: (a) Subject 1, (b) Subject 2 and (c)

32

Subject 3. The vertical scale between subjects is not equal. Feedback training substantially

reduced head motion during and after training. Note the major improvement in inferior-superior

motion, where the majority of displacement occurred prior to training.

3.3.2 Cohort Study

3.3.2.1 Simulator Data

The cohort study revealed differences in head motion between healthy young and old

individuals, and stroke patients, as well as benefits from simulator training. The results are

summarized in two formats. Figure 7 shows bar plots of AD and CD values for all healthy

young, healthy old, and stroke patients that received simulator training, reported as a function of

simulator run (Pre, Train1, Train2, and Post). Figures 8, 9, and 10 show scatter plots for CC

values as a function of simulator run as well as for head motion observed during fMRI for hard

and easy task conditions, and the associated behavioural data. As observed in Fig. 7, the groups

exhibited different amounts of head motion prior to simulator training. A one-way ANOVA

between young and elderly subject groups revealed a significant difference in AD (P < 0.001),

but no significant difference in CD (P = 0.50). Pre-training group differences in CC (Figs. 8 and

9) were also not statistically significant (P = 0.26). On further detailed inspection of the data,

these AD, CD, and CC effects were due to differences in the nature of head motion observed in

the two groups. Healthy young adults mainly exhibited random fluctuations in head motion

whereas healthy elderly adults exhibited random head motion as well as systematic slow motions

well described by linear trends. These effects lead to a separation of the groups in terms of AD,

an average parameter, whereas the cumulative motion exhibited by each group on a point-by-

point basis, as captured by CD values, was very similar. In comparison, the stroke group

exhibited average AD values intermediate between those of the young and elderly groups, and

average CD values that were very similar to those of both groups. However, the CC values were

33

markedly higher in the stroke group prior to simulator training, indicating the presence of

substantial task-correlated motion (see below for further details). Lastly, Fig. 7 also shows the

absence of benefit in terms of AD and CD values for all three groups. Instead of a reduction in

AD and CD values, both remain constant across Pre, Train1, Train2, and Post training runs

within experimental error for each group, with a statistically significant difference between AD

values maintained for healthy young and elderly groups (P < 0.05).

Figure 7: Plots for the three subject groups in the simulator are shown for (a) Absolute deviation

(AD) and (b) Cumulative deviation (CD). Error bars represent standard error of the mean.

Perusal of Figs. 8-10 indicate that some subjects benefited from simulator training to reduce

task-correlated head motion, as quantified by CC values. In particular, Fig. 8a clearly shows a

training benefit for healthy young subjects. A post-hoc one-tailed paired T-test showed a

significantly reduced CC value (P < 0.05) for Pre and Post training runs, with CC values after

34

training showing no significant difference from those untrained young adults that exhibited

minimal head motion at the outset. Although the healthy elderly subjects did not show a

simulator training benefit based on CC value (Fig. 9a), the stroke group showed a substantial

improvement in CC after training, progressing from values indicative of highly task correlated

motion, to minimal correlations very similar in magnitude to those of young healthy subjects.

3.3.2.2 fMRI Data

Overall, simulator training either had positive or no impact on fMRI results, and no negative

impact, for hard and easy conditions. An ANOVA between the young and elderly showed a

statistically significant difference in AD (P < 0.05) and CD (P < 0.01), but no significant

difference in CC (P = 0.41). Comparison of trained and untrained groups for young and elderly

showed no significant difference in any of the three motion parameters during fMRI for hard and

easy conditions, with CC values shown in Fig. 8b and 9b, respectively. The benefit of simulator

training on CC value was retained for the healthy young adults during actual fMRI (Fig. 8b) and,

interestingly, the CC value for the healthy elderly group was reduced for both hard and easy

conditions during actual fMRI experiments in comparison to the values obtained in the simulator

(Fig. 9b). The stroke group also retained the training benefit from the fMRI simulator in terms

of CC value (Fig. 10b), with AD values intermediate between and CD values similar to the two

healthy groups.

35

Figure 8: Correlation values (CC) plotted for healthy young subjects in the (a) simulator and

during (b) fMRI. Corresponding behavioural data are given in (c) with respect to the task

performed during fMRI. All error bars represent the standard error of the mean.

Figure 9: Correlation values (CC) plotted for healthy elderly subjects in the (a) simulator and

during (b) fMRI. Corresponding behavioural data are given in (c) with respect to the task

performed during fMRI. All error bars represent the standard error of the mean.

36

Figure 10: Correlation values (CC) plotted for stroke subjects in the (a) simulator and during (b)

fMRI. Corresponding behavioural data are given in (c) with respect to the task performed during

fMRI. All error bars represent the standard error of the mean.

A three-way repeated measures ANOVA comparing easy and hard difficulties, showed a

statistically significant difference in AD (P < 0.001) across all young and elderly subjects and

between young and elderly groups (P < 0.05).

Regarding behavioural data captured during fMRI, all subjects performed the easy and hard tasks

well, with no statistically significant difference between the groups (average time in target: 1.18

+/- 0.31s easy; 1.33 +/- 0.40s hard). There was no statistically significant difference between

trained and untrained groups for the young and elderly. A slight increase in performance from

easy to hard was observed in the young group for both trained and untrained conditions, while a

slight decrease presented in the stroke group. No change was observed in the elderly trained

group for task difficulty, but the untrained elderly recorded slightly poorer initial performance

that improved in the second run to be comparable with the trained group. Overall, the

performance data in Figs. 8-10 suggested that there was no substantial difference in difficulty

37

between the two conditions. Qualitative data is shown in Table 2 as a second measure of

difficulty provided by the subjects.

Table 2: Ratings from the subject groups on the difficulty of the two task conditions during

fMRI, where 1 is very easy and 8 is very difficult (mean +/- standard error).

Subject Group Easy Condition Hard Condition

Young Trained 1.5 +/- 0.4 2.8 +/- 0.4

Young Untrained 1.4 +/- 0.2 2.2 +/- 0.2

Elderly Trained 2.0 +/- 0.3 3.4 +/- 0.5

Elderly Untrained 2.0 +/- 0.4 2.5 +/- 0.7

Stroke Trained 3.0 +/- 0.6 5.0 +/- 1.0

3.3.2.3 Activation Maps and Voxel Counts

As expected, the fMRI data revealed activations primarily in the contralateral primary

sensorimotor region of the brain (Fig. 11). Due to the high variability in brain activation

associated with human testing, and the relatively small sample size of each of the subgroups

(trained and untrained) for healthy young and old, there is insufficient statistical power to assess

quantitatively whether use of the simulator impacted brain activity. However, several qualitative

observations are notable, based on visual inspection of the activation maps. In the young group,

some of the trained subjects had higher counts of activated voxels than untrained subjects. Brain

activity appears to reduce in the second, more difficult task condition across young and elderly

subjects in the sensory-motor region of interest. The stroke group exhibited more bilateral

activation in the motor and sensory cortices, and on the whole more non-localized activity. One

stroke subject that suffered right side paresis showed activity in the right side of the motor cortex

(Fig. 11e) while performing the task with his affected hand (ipsilateral activation), that being

indicative of brain compensation in stroke recovery.

38

Analysis of total activated voxels correlated with motion showed no statistically significant

difference between group, training, difficulty, or combination thereof. For task correlated

voxels, a three-way repeated measures ANOVA showed a significant difference between young

and elderly groups (P < 0.05) with young showing more activity than elderly, but no significant

effect from difficulty or training.

Figure 11: Representative brain activity for: (a) young trained, (b) young untrained, (c) elderly

trained, (d) elderly untrained and (e) stroke individual subjects. Note the ipsilateral activity of

the stroke subject with right side paresis. Family-wise error rate was set at P = 0.001 with

nearest neighbour clustering at 20 voxels minimum volume. Colour scale is representative of t-

value.

3.4 Discussion

The results presented here add considerable insight to the limited existing scientific literature

based on the use of a simulator as a training tool to remove head motion artifact, particularly the

work done by Seto et al. in 20016. Their work characterized the head motion of different subject

populations while performing a motor task, which brought light to the relationship between

subject type and fMRI data corruption due to motion. From basic coaching in the simulator of

one stroke subject, resulting in decreased head motion, it was suggested that a simulator could be

used as a low cost tool to prevent motion artifact in fMRI. The present work evaluates the

efficacy of using a simulator to decrease head motion artifact by studying the effect of visual

39

feedback training across young, elderly and stroke subject populations. Before discussing the

implications of the thesis work, it is prudent first to place them in context by mentioning two

experimental design issues.

First, the head motion characteristics observed in the experiment result from subjects performing

specific motor tasks involving unilateral gripping. Logistically, these tasks were chosen because

the associated hardware and software was conveniently available and assembled for an imminent

fMRI application study involving stroke patients. Although these tasks are typical of the type

employed in fMRI studies, many other tasks are possible which could elicit more or less motion,

or motion of a slightly different character. Consequently, the quantitative information provided

regarding head motion and simulator training must be considered only as a broad guide for

consideration when designing future fMRI studies. Related to this point, it is also important to

consider the challenge in making tasks equivalently difficult over different subject groups and

patient populations. In particular, it is very likely that the stroke patients found task performance

during fMRI (either the easy or the hard) more difficult and effortful than did the healthy elderly

or young subjects. This is perhaps part of the reason why stroke subjects exhibited the highest

level of task-correlated motion. Depending on the nature of specific hypotheses on brain

activation, an alternative approach to recommending that all stroke patients receive fMRI

simulator training is simply to design tasks that are sufficiently easy for stroke patients to

perform, such that head motion is minimized. For the present study, the interpretation of Figs. 7

- 10 should be considered primarily on a within-age, and within-patient group basis, with easy

and hard tasks included to determine whether simulator training effects were negatively

influenced by the specific increase in task difficulty.

40

Second, only one method of feedback training was employed during this initial study, for the

practical reason that investigation of feedback training methods would require substantially more

subjects and data collection. The scope of the experiments and multiple subject cohorts included

in the present work is already quite large in the context of fMRI studies. The positive results

observed in the study do warrant future studies involving simulator training with different

feedback strategies, in an attempt to minimize the head motion artifacts present in fMRI signals.

With these provisos, the results are discussed with focus on the effect of training within the

simulator environment and the corresponding comparison of trained and untrained individuals

during fMRI. Particular interest is on task correlation and two important thresholds for

problematic motion: motion amplitude and motion correlation exceeding 1 mm8,9

and r = 0.3,

respectively. Initial motion results are compared with the previous work done by Seto et al., but

the results of simulator feedback training presented here are new to the literature.

3.4.1 Pilot Study

The results from the stroke pilot group indicated that simulator training had a substantial effect

on the prevention of head motion during shortened event related runs. Prior to training, the range

of motion exhibited by the group averaged 5.8 mm, a substantial increase from 2.0 mm reported

by Seto et al. for unrestrained stroke subjects in a hand gripping task, but this small data set was

biased by one subject whose motion exceeded 10 mm. More importantly, the present data agree

with Seto et al. on the grounds that stroke subjects, given a motor task, exhibit motion that would

result in high amounts of fMRI data corruption from artifact.

Figure 6 shows raw positional data recorded in runs before, during and after feedback training. It

is clear that the majority of motion prior to training was in the inferior-superior direction with

evidence of sharp displacements corresponding with the task stimulus. During the second

41

training run, the amount of motion in all directions was substantially diminished. Decreased

motion in the inferior-superior direction is especially important because it is considered through-

plane and can result in spin history artifact. After training (Figure 6), the reduction in head

motion held for the group, with averaged motion range reducing to only 0.67 mm. These data

helped confirm the anecdotal findings of Seto et al. for coaching head motion with stroke

subjects in a simulator and warranted further development and investigation of simulator training

using a cohort study.

3.4.2 Cohort Study

3.4.2.1 Motion in the Simulator

Initial motion results across the young, elderly and stroke groups are consistent with those

observed by Seto et al., who observed that young adults exhibited minimal head motion, elderly

adults exhibited moderate head motion, and stroke patients exhibited large levels of problematic

head motion. Figure 7 shows results across all groups for the AD and CD motion parameters,

whereas initial motion results for the CC parameter are shown in Figs. 8a, 9a, and 10a. In terms

of the AD parameter, the elderly group exhibited larger motion than the young, which was

confirmed significant in the ANOVA analysis. The stroke patient cohort, although small and not

amenable to statistical analysis, nevertheless exhibited smaller AD values than the healthy

elderly, but substantially worse task-correlated motion than either the healthy young or the

healthy elderly group.

In comparing the two studies further, it is notable that aside from investigating different subject

samples, this study and that of Seto et al. also have two differences in the techniques used for

quantifying head motion. The former study includes use of the CC parameter to characterize

task-correlated motion, whereas the latter did not. In addition, although Seto et al. used a metric

42

very similar to the AD parameter used in the present study, they performed a regression to

remove linear trends prior to quantifying head displacement. The rationale for performing this

pre-processing step was that, for the most part, linear trends in fMRI data are not largely

problematic and can be similarly eliminated. In the present study, the data for healthy adult

subjects were visually inspected and confirmed to exhibit substantial slow motion trends, which

explained the large AD values in comparison to both the healthy young subjects and stroke

subjects.

Simulator training was received well by the young and stroke groups, evident in the reduction of

task correlated motion observed in Figs. 8b and 10b. The young and stroke groups were able to

decrease CC steadily and substantially through training to values below 0.3, the threshold set for

substantial task-correlated motion (Table 4). This training effect did not include reduction of

slow motions or random fluctuations, as neither AD values nor CD values showed similar

training effects (Fig. 7). Interestingly, the elderly group showed no substantial response to

training in any of the motion parameters. The lack of improvement by the elderly group could be

because the relative contribution of task correlated motion was small at the outset, and

dominated by random motion and slow variations that are less amenable to simulator training.

Alternatively, the stroke subjects were observed anecdotally to be more aware of their

involuntary head motion and tried hard during the tasks to keep their head still (harder than

healthy elderly subjects) because they could identify with the task as a personal accomplishment

in spite of their disability.

3.4.2.2 Motion during fMRI

The trained groups exhibited head motion during fMRI that was similar to that observed in the

simulator. This is an important finding, indicating that fMRI simulator training benefits

43

achieved for healthy young subjects and for stroke patients extend to actual imaging

examinations (Figs 8 and 10). In the case of the healthy young subjects, trained individuals

showed similar head motion during fMRI as those that did not require simulator training at the

outset. Trained elderly subjects showed head motion similar to their untrained counterparts (Fig.

9). In this instance, simulator training produced no benefit, but at least did not have a

detrimental effect. One difference between simulator and fMRI motion results was that all

groups showed substantially less task correlation during the imaging examination. This could be

an effect of the foam wedges used as a light restraint, not present in the simulator. Also, rigid

body motion estimates from fMRI were collected with a very low sample rate (0.5 Hz), where

motion affecting the CC measure could have been missed. Because of this, a direct comparison

between simulator and fMRI CC values is not advised and therefore plotted separately in Figs. 8-

10.

Behavioural data from the fMRI tasks showed that there was no substantial effect of training on

task performance. It was a potential concern that trained subjects, especially stroke, would

neglect the task when focusing on keeping their head still. Figure 10c shows that the stroke

subjects were able to perform the easy and hard tasks adequately, despite having to focus on

keeping their head still. Both healthy young groups showed increased performance during the

hard task, but this is attributed as a task order effect arising from acquired learning during the

easy condition. This effect could be removed in subsequent studies by randomizing the order in

which easy and hard tasks were administered during fMRI. No substantial effect of difficulty on

performance of the elderly groups was present.

44

3.4.2.3 Voxel Counts

Analysis of the motion and task correlated voxel counts revealed little to support an effect of

simulator training. These data were highly variable because many of the subjects showed small

amounts of brain activity from the tasks, given their relatively simple nature. The primary effect

observed was the trained healthy young subjects had approximately 78% more task-activated

voxels than the untrained, for both easy and hard conditions. Analyzed using the same family-

wise error rate, this suggests that the statistical variance was higher in the untrained group,

resulting in fewer voxels being detected as significant. The motion metrics do not explain this

finding because the difference between trained and untrained young was negligible for all

parameters. However, it is possible that the effect is represented by other motion metrics, as

those utilized only provide very basic details about head movement and motion components at

the task frequency. It should also be noted that the motion estimates generated from time series

coregistration were taken with an effective sampling rate of 0.5 Hz (2 s TR). This left large

intervals during imaging when rapid motion could have occurred and not been detected. Motion

between scans in the time series data, especially in the through-plane direction, can cause

residual motion artifacts do to the spin history effect. It is possible that the resultant activation

data may have been affected by motion occurring between scans, resulting in a higher variance

between voxels and lowering detection sensitivity of BOLD.

45

4 Conclusions

4.1 Aim 1

A simulator was developed to provide an MRI-like environment where adult subjects could be

evaluated and trained to limit head motion. The simulator had identical physical parameters as a

real Siemens Tim Trio MRI system. A training protocol was developed based on a visuo-motor

task involving unilateral hand gripping. Training provided real time visual feedback of head

motion while the subject performed a gripping task following an auditory stimulus. The training

protocol was designed to have the subject perform four shortened runs representative of the

actual fMRI task. The two middle runs provided visual feedback of head motion whereby the

subject was instructed to stay within a target area representing approximately 1 mm. Total time

for simulator training with one operator was between 30 minutes to 1 hour depending on the

level of assistance required by the subject.

4.2 Aim 2

Three motion metrics were calculated from positional data to characterize performance of

subjects with respect to head motion. Absolute deviation (AD) was a measure of central

tendency of motion from the initial position. Correlation coefficient (CC) was derived from a

test waveform meant to model hemodynamic response to the square wave task stimulus,

quantifying the extent that motion was task correlated and lead to false positive estimates of

brain activity. Finally, cumulative point-to-point deviation (CD) was a measure of total motion.

The three motion parameters were used to test the effect of training on young, elderly and stroke

subjects. Initial results corresponded well with existing literature for between group effects.

Training was well received by young and stroke subjects, but failed to have a substantial effect

46

on the elderly. The most sensitive metric to training was CC, which indicated a substantial

decrease in task correlated motion among the young and stroke subjects through training.

Imaging data were analyzed using Analysis of Functional NeuroImages (AFNI) and the effect of

training on brain activity was investigated. No significant effect of training was shown. This

was partly due to large data variability from the relatively small human cohorts for fMRI.

Anecdotal findings showed reduced brain activity in untrained young when compared to the

trained group, although these findings were not mirrored in AD, CC, or CD values.

4.3 Significance of Work

This line of research offers an alternative method of reducing head motion before it becomes a

problem in fMRI data acquisition. The literature shows that head motion in fMRI, especially

that which is large or task correlated, is often difficult to correct once it has occurred. Current

prospective methods of reducing head motion artifact are not without their respective challenges

and often require modification to the scanning workflow. The only drawbacks to using a

simulator are space (which may be a high commodity in imaging labs) and training time (which

should be kept as short as is feasible to avoid fatiguing subjects). If there is little floor space

available at a given imaging facility, simulators much smaller than the apparatus used in this

thesis have proven effective at representing a stressful MRI-like environment to adult subjects.

This work has shown that a simulator and feedback training have the capability to reduce head

motion in problematic motor stroke subjects and even well performing young subjects. All six

stroke subjects evaluated in the pilot and cohort study showed substantial improvement in task-

correlated motion. Those exhibiting a large range of motion initially were able to limit motion to

below the accepted threshold for fMRI data corruption after training. These results, from a very

47

basic training protocol, provide a basis for future work to improve simulator training design and

optimize its effect on subject head motion.

4.4 Future Work

The training protocol presented in this thesis should be investigated further. The results showed

that training provided the greatest benefit to the stroke group. Thus, a study dedicated only to

stroke subjects is warranted to confirm the results presented here. In a study focusing

experimental resources on one subject population, more can be investigated. For instance, it

should be feasible to prove specific mechanisms of training, long-term effects, different tasks,

and effectiveness of different protocol designs. Some thoughts on optimizing the training

protocol for future work are given below.

Future work should include refinement of the user interface, particularly the stimulus display of

visual feedback. Some subjects reported that they found the scrolling lines difficult to follow.

This is understandable because head motion in three directions was represented by vertical

displacements of lines, which the subject had to learn. Improvement might be achieved by

replacing the existing scrolling graph with a 3D-like representation of the subjects head position.

A cursor could be represented with movement on the display representing inferior-superior and

medial-lateral motions, with the size of the cursor acting as the third dimension for anterior-

posterior displacement. In this case, the motion fed back to the subject would mirror their head

motion better, making it more intuitive to understand and potentially to control.

Another possible modification to the feedback design would be to replace the multidimensional

visual stimulus with an auditory one. This would provide a simpler, one-dimensional feedback

stimulus for subjects who have difficulty understanding the visual representation of motion. A

tone could be used that represents the magnitude of motion with volume. Initially the tone is low

48

and increases in volume with motion away from the start position. This would also allow for

motion feedback to be delivered simultaneously with visual tasks.

This thesis provides a basis for future work with fMRI simulators to be used as a tool for

reducing head motion, in order to supplement current fMRI exam protocols and motion

correction techniques. Simulator training can be used to reduce head motion in problematic

subject populations at the outset, lowering the amount of residual motion artifact that needs to be

handled in post processing.

49

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