Download INVESTIGATING COGNITIVE IMPAIRMENTS IN AMYOTROPHIC LATERAL

INVESTIGATING COGNITIVE IMPAIRMENTS IN AMYOTROPHIC LATERAL
SCLEROSIS (ALS) USING EYE MOVEMENTS AND FUNCTIONAL MAGNETIC
RESONANCE IMAGING (FMRI)
By
Kelsey Louise Marie Witiuk
A thesis submitted to the Graduate Program in the
Centre for Neuroscience Studies
in conformity with the requirements for the
Degree of Master of Science
Queen’s University
Kingston, Ontario, Canada
(September, 2011)
Copyright © Kelsey Louise Marie Witiuk, 2011
Abstract
Patients with Amyotrophic lateral sclerosis (ALS) often experience cognitive impairment that
accompanies degeneration of the motor system. A valuable tool for assessing cognitive control
over behaviour is the antisaccade task which requires: 1) inhibition of the automatic response to
look towards an eccentric visual stimulus (prosaccade) to instead 2) redirect gaze in the opposite
direction of the stimulus (antisaccade). Psychometric tests were used to quantify the degree of
impairment, while eye tracking, functional magnetic resonance imaging (fMRI) and structural
MRI were combined to identify the neural correlates of cognitive impairment in ALS. We predict
ALS patients will have executive dysfunction and grey matter loss in executive and oculomotor
control areas that will affect antisaccade performance and will alter the corresponding brain
activation. ALS patients and age-matched controls participated in a rapid-event-related fMRI
design with interleaved pro- and antisaccade trials. Catch trials (no stimulus presented after
instructional cue to prepare pro- or antisaccade) allowed us to discern the preparatory period
from the execution period. ALS patients were biased towards automatic saccade responses, and
had greater difficulty with antisaccades relative to controls in terms of correct and timely
responses. We found that worsened antisaccade performance in ALS correlated with the degree
of cognitive impairment. Generally, we found trends of increased brain activation during the
preparatory period of antisaccades in ALS patients compared to controls in most oculomotor
areas; meanwhile few differences were seen during execution. Structural analyses revealed ALS
patients had decreased grey matter thickness in frontotemporal and oculomotor regions such as
the frontal and supplementary eye fields (FEF, SEF) and the dorsolateral prefrontal cortex
(DLPFC). These findings suggest that loss of structural integrity and executive dysfunction may
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elicit compensation mechanisms to improve functional and behavioural performance. Despite
this compensation, ALS patients still performed worse on antisaccades than controls. Further
investigation to expand the current data set should improve our ability to assuredly identify the
neural correlates of cognitive decline in ALS, and may provide a model system to use for critical
evaluation of future therapies and interventions for ALS.
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Co-Authorship
This research was carried out by Kelsey Witiuk under the supervision of Dr. Douglas P.
Munoz and Dr. Michel Melanson. The idea for this research project was conceived by Dr.
Douglas Munoz and Dr. Michel Melanson, and was originally started by a medical student, Dr.
Ryan McKee. Dr. Michel Melanson recruited participants with ALS from his neuromuscular
clinics at Saint Mary’s of the Lake and Kingston General Hospitals. Dr. Ryan McKee collected
preliminary data from eight ALS patients, while Kelsey Witiuk collected the remaining ALS
participants, and performed all of the data analyses and writing of this thesis.
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Acknowledgements
I would first like to express my deep gratitude for the opportunity to work under the
primary supervision of Dr. Douglas P. Munoz for my Master’s thesis. I will be forever
appreciative of the guidance, kindness and mentorship that Doug has provided me during the
course of my MSc. Doug’s passion for neuroscience is infectious, and is clearly reflected by the
dedicated members of his lab, and the high-calibre research that is produced. It has been a
privilege to be a part of the Munoz lab extended family over the last 2 years.
I would also like to extend great thanks to my secondary supervisor Dr. Michel Melanson for
providing me with the unique opportunity to work directly with ALS patients, an experience that
has provided me with invaluable clinical and life skills. I truly appreciate Michel’s hard work on
patient recruitment, and for encouraging my interests in neurology that extend beyond the
requirements of my project.
To my lab mates: Dr. Ian Cameron, Dr. Alicia Peltsch, Dr. Nadia Alahyane, Rebecca
Schwerdtfeger and Stephen Lee, thank you for all of your academic help and continuing
friendship. In particular, I must thank Nadia, Ian, Alicia and Rebecca for ‘showing me the ropes’
of the Munoz lab, and for being the clinical fMRI and Nemo-task ‘trail-blazers’. I would like to
thank Dr. Brian Coe, my go-to ‘Mr. Fix-It’, for his endless hours of tech support, help with fMRI
data acquisition, and many laughs. I would like to thank Don Brien for his programming support
and help with the structural analyses. I am also grateful for the administrative help offered by
Ann Lablans, Lucy Russo-Smith and Kelly Moore.
I must extend a warm thank you to the ALS patients who participated in this study, and to
their families for bringing them to appointments, and for providing support along the way.
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Without their combined generosity, cooperation and patience, this research would never have
taken place. It was truly a pleasure working with them, and their courage and resilience has
provided me with great inspiration to remain dedicated to this research, even when
discouraging obstacles were thrown my way.
Additionally, I would like to acknowledge the ALS Society of Canada for their great work, not
only helping families affected by ALS, but also for their fundraising efforts at events like the ALS
Walk. I had the honour of presenting my research at one of their fundraising events, and I am
very appreciative of their enthusiasm towards ALS research. During their annual ALS Society of
Canada conference, which I had the privilege of attending the past two years, I have gained
invaluable experiences both academically and personally.
Most importantly, I must graciously thank my own support team. To my parents Sid and
Karole, my sister Sarah and my extended family, thank you all for your words of encouragement,
endless love and support. To my girls back home, thank you for always being there, for believing
in me, and for trying to understand my long rants about my research. I must also give special
thanks to Steve Lund for his unconditional support and friendship throughout this project,
without which I would have been hard-pressed to make it to the finish line.
My apologies if I have inadvertently omitted anyone to whom acknowledgement is due. For
any errors or inadequacies that may remain in this work, of course, the responsibility is entirely
my own. This research was supported by operating grants from the Canadian Institutes of
Health Research (CIHR) and the Bernice Ramsay Discovery Grant from the ALS Society of
Canada.
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Dedication
This thesis is dedicated to my late grandmother, Kathleen “Louise” Margaret Witiuk.
“You can dig a ditch (for a living) if you want,
but it’s going to be an educated ditch.”
-
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Grandma
Table of Contents
Abstract ............................................................................................................................................ ii
Co-Authorship ................................................................................................................................. iv
Acknowledgements.......................................................................................................................... v
Dedication ...................................................................................................................................... vii
Chapter 1 General Introduction ....................................................................................................... 1
1.1 Clinical and Diagnostic Features ............................................................................................ 3
1.2 Etiology .................................................................................................................................. 4
1.3 Cognitive Impairment in ALS .................................................................................................. 7
1.3.1 Evidence of an ALS-FTLD continuum ............................................................................... 8
1.3.2 The importance of neuropsychological testing ............................................................. 10
1.4 Saccadic Eye Movement System .......................................................................................... 12
1.4.1 The oculomotor circuit .................................................................................................. 14
1.4.2 Express saccades ........................................................................................................... 17
1.4.3 Preparatory set ............................................................................................................. 19
1.4.4 Eye movements in ALS................................................................................................... 20
1.5 Neuroimaging in ALS ............................................................................................................ 24
1.6 Thesis Objectives.................................................................................................................. 26
Chapter 2 Investigating cognitive impairments in amyotrophic lateral sclerosis using saccadic eye
movements and functional magnetic resonance imaging ............................................................. 28
2.1 Introduction ......................................................................................................................... 28
2.2 Methods ............................................................................................................................... 31
2.2.1 Subjects ......................................................................................................................... 31
2.2.2 Clinical Evaluation ......................................................................................................... 35
2.2.3 Saccade paradigm ......................................................................................................... 37
2.2.4 Eye tracking and visual display ..................................................................................... 40
2.2.5 Functional magnetic resonance imaging parameters .................................................. 41
2.2.6 Behavioural analysis ..................................................................................................... 41
2.2.7 Functional analysis ........................................................................................................ 43
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2.2.8 Structural analysis ......................................................................................................... 46
2.3 Results .................................................................................................................................. 47
2.3.1 Clinical and Neuropsychological Evaluation ................................................................. 47
2.3.2 Behaviour ...................................................................................................................... 49
2.3.3 fMRI............................................................................................................................... 54
2.3.4 Structural Analysis......................................................................................................... 68
2.3.5 Correlation Analysis ...................................................................................................... 71
2.4 Discussion............................................................................................................................. 78
2.4.1 Influence of Cognitive Impairment ................................................................................ 79
2.4.2 Saccades ........................................................................................................................ 81
2.4.3 Neural Circuitry ............................................................................................................. 85
2.4.4 Structural Integrity ........................................................................................................ 91
2.4.5 Functional Compensation ............................................................................................. 92
2.4.6 Limitations and Future Directions ................................................................................. 95
Chapter 3 General Discussion ........................................................................................................ 97
3.1 Clinical Relevance................................................................................................................. 98
3.2 Limitations.......................................................................................................................... 100
3.3 Future Directions ............................................................................................................... 103
3.4 Summary and Conclusions ................................................................................................. 104
Appendix A ................................................................................................................................... 139
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List of Figures
Figure 1.1: Schematic of prosaccade and antisaccade tasks ......................................................... 13
Figure 1.2: Schematic of the oculomotor circuit ........................................................................... 15
Figure 2.1: Experimental paradigm ................................................................................................ 39
Figure 2.2: Cumulative distribution of saccadic reaction times..................................................... 50
Figure 2.3: Behaviour. .................................................................................................................... 52
Figure 2.4: Saccade metrics. .......................................................................................................... 54
Figure 2.5: Anti- and prosaccade contrast map ............................................................................. 56
Figure 2.6: Saccade preparation contrast maps ............................................................................ 59
Figure 2.7: Region of interest (ROI) analysis for pro and antisaccade preparation ....................... 61
Figure 2.8: Saccade execution contrast maps ............................................................................... 64
Figure 2.9: Region of interest (ROI) analysis for pro and antisaccade execution .......................... 66
Figure 2.10: Significance level of cortical thickness ....................................................................... 69
Figure 2.11: Volumetric analysis .................................................................................................... 71
Figure 2.12: Correlations between antisaccade measures and mean peak beta weights during
anti preparation and execution ..................................................................................................... 74
Figure 2.13: Correlations between grey matter volume and peak beta weights .......................... 76
x
List of Tables
Table 1: Clinical information for all recruited ALS patients ........................................................... 32
Table 2: Talairach coordinates of peak activation for antisaccade + prosaccade contrast ........... 57
Table 3: Talairach coordinates of peak activation for prosaccade and antisaccade preparation
contrasts ........................................................................................................................................ 60
Table 4: Talairach coordinatesof peak activation for prosaccade and antisaccade execution
contrasts ........................................................................................................................................ 65
Table 5: Pearson correlations between neuropsychological evaluations ..................................... 81
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List of Equations
Equation 1: Verbal fluency calculation (Abrahams et al., 2000). ................................................... 36
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List of Abbreviations
AC-PC
anterior commissure – posterior commissure
ALS
Amyotrophic lateral sclerosis
ALSFRS-R
Amyotrophic lateral sclerosis Functional Rating Scale Revised
ANOVA
Analysis of variance
BG
basal ganglia
BOLD
blood oxygen-level dependent
2+
Ca
calcium ion
CD
caudate nucleus
CIHR
Canadian Institutes of Health Research
CNS-LS
Centre for Neurologic Study Lability Scale
COWA
Controlled Oral Word Association
COGNISTAT
Neurobehavioural Cognitive Status Examination
CSF
cerebrospinal fluid
CT
computed tomography
CVSRT
coefficient of variation of SRT
DLPFC
dorsolateral prefrontal cortex
DTI
diffusion tensor imaging
EPI
echo-planar imaging
FA
flip angle
FBI
Frontal Behavioural Inventory
FDA
Food and Drug Administration
FEF
frontal eye fields
fMRI
functional magnetic resonance imaging
FOV
field of view
FP
fixation point
FTD
frontotemporal dementia
FTLD
frontotemporal lobar degeneration
FUS/TLS
fused in sarcoma/translated in liposarcoma
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FVC %pred
forced vital capacity percent predicted
FWHM
full width at half maximum
GLM
general linear model
GM
grey matter
HADS
Hospital Anxiety and Depression Scale
LGN
lateral geniculate nucleus
LMN
lower motor neuron
LPF
lateral prefrontal
MMSE
Mini Mental State Examination
MND
motor neuron disease
MNI
Montreal Neurological Institute
MoCA
Montreal Cognitive Assessment
MP-RAGE
Magnetization Prepared Rapid Gradient Echo
MRI
magnetic resonance imaging
MVIC
maximal voluntary isometric contraction
PD
Parkinson’s disease
PEF
parietal eye fields
PET
positron emission tomography
PL
parietal lobe
PPRF
paramedian pontine reticular formation
rCBF
regional cerebral blood flow
SC
superior colliculus
SCi
intermediate layers of superior colliculus
SCs
superficial layers of superior colliculus
SD
standard deviation
SE
standard error
SEF
supplementary eye fields
SOD-1
superoxide dismutase-1
SPECT
single photon emission computed tomography
SRT
saccadic reaction time
xiv
T
target
TDP-43
TAR-DNA-binding protein 43
TE
echo time
TR
repetition time
TTL pulse
transistor-transistor logic pulse
UMN
upper motor neuron
VF
verbal fluency
WM
white matter
†, ‡, *, **
to highlight statistical significance where described
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Chapter 1
General Introduction
Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease (MND), is a
progressive neurodegenerative disease affecting motor neurons in the cerebral cortex,
brainstem and spinal cord. The neuropathology of ALS is marked primarily by degeneration of
upper motor neurons (UMN) in the brainstem and motor areas of the cortex, and of lower
motor neurons (LMN) in the brainstem and spinal cord (Brooks, 1994; Brooks, Miller, Swash, &
Munsat, 2000). This is accompanied by a loss of alpha motor neurons in the anterior horn of the
spinal cord and gliosis of the corticospinal tracts, the former causing voluntary muscles in the
body to become denervated (Abrahams, Goldstein, Kew, & Brooks, 1996; Hofmann, Ochs, Pelzl,
& Warmuth-Metz, 1998; Mu, He, Anderson, Springer, & Trojanowski, 1996). Denervation
generates physical symptoms of muscle weakness, atrophy and loss of tone (Mu et al., 1996;
Murphy, Henry, & Lomen-Hoerth, 2007a), which progress until the production of voluntary
movement becomes impossible. ALS is more common in men than women (1.6:1) (Mitchell &
Borasio, 2007), and most often affects people in their sixties. Women tend to have a later onset
(Haverkamp, Appel, & Appel, 1995) which is associated with a worse prognosis than those who
are diagnosed at a younger age (A. D. Rosen, 1978). In the majority of cases, patients rapidly
decline in function and succumb to respiratory failure within 2 to 5 years of symptom onset
(Hillel & Miller, 1989; Kwiatkowski et al., 2009; Mitchell & Borasio, 2007).
Historically, motor impairments have highlighted the progressive disability seen in ALS, with
relative sparing of the sensory system (Brooks, 1994). Traditionally, ALS was thought to affect
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the motor system but leave all cognitive functions intact; however within the last few decades
there has been consistent evidence that cognitive impairments can be detected within a large
proportion of patients (for review see: (Neary, Snowden, & Mann, 2000; Phukan, Pender, &
Hardiman, 2007; Woolley & Katz, 2008)). Despite this growing body of evidence, the diagnostic
criteria for ALS continue to omit the requirement of any cognitive symptoms for a positive
diagnosis; diagnosis relies solely on confirmation of the physical symptoms associated with
motor neuron loss.
With this increasing recognition of cognitively impaired individuals within the ALS
population, it is an opportune time to explore this facet of the disease and gain new insights into
how ALS is viewed and diagnosed. Because a decline in cognitive function can sometimes
precede the onset of physical symptoms in ALS (Guedj et al., 2007; Mackenzie et al., 2005;
Murphy et al., 2007a), it is likely that testing a patient’s cognitive status can serve as a valuable
new screening tool, particularly for individuals with a family history of ALS. This thesis intends to
document cognitive deficits identified through a combination of neuropsychological tests and
other reliable tools, such as saccadic eye movement tasks designed probe executive function, or
neuroimaging techniques such as functional and structural magnetic resonance imaging (MRI).
In order for this multi-disciplinary method of studying cognitive impairment to be useful for
disease diagnosis, we must first understand and document the fundamental cognitive,
behavioural, functional and structural changes that can be expected over time when someone is
afflicted with ALS. This thesis aims to investigate and record these changes in as many ALS
patients as possible within the limited timeframe of a Master’s program.
2
1.1 Clinical and Diagnostic Features
The most widely-accepted diagnostic criteria for ALS are the Revised El Escorial Criteria (see
Appendix A), set by The World Federation of Neurology (Brooks et al., 2000) which were
adapted slightly from the original criteria (Brooks, 1994) to increase their overall sensitivity.
Location of symptom onset can vary from bulbar (brainstem) to cervical, thoracic or lumbosacral
regions of the spinal cord, however a progressive spread of impairment from one region to
another is necessary for diagnosis (Brooks et al., 2000). There is a lack of reliable biomarkers for
ALS. Hence, the El Escorial criteria have established several classifications that quantify the
certainty of diagnosis as determined by neurological exams, standard electromyographic
needles and nerve conduction studies (Brooks et al., 2000). To receive a diagnosis of clinically
‘definite’ ALS, the patient must display both UMN (spastic muscle tone, exaggerated reflexes
and clonus) and LMN (muscle weakness, atrophy, fasciculations) signs in the bulbar region and
in at least two other spinal regions, or, alternatively, the presence of UMN and LMN symptoms
in three spinal regions (Brooks et al., 2000). Clinically ‘probable’ ALS is diagnosed when UMN
and LMN symptoms are present in at least two regions, with some UMN symptoms presenting
rostrally to the LMN symptoms (Brooks et al., 2000). Diagnosis can only be confirmed in the
absence of neuroimaging or electrophysiological findings supporting other disease processes
that would explain the impairments (i.e. oesophageal carcinoma or myasthenia gravis) (Brooks
et al., 2000; Mitchell & Borasio, 2007).
Patients can be categorized into sub-clinical groups based on the onset location of their
symptoms. An initial loss of UMN, LMN, or both in the brainstem is associated with bulbar-onset
ALS and is usually marked by a loss of cranial nerve functions (Hillel & Miller, 1989). Bulbar3
onset patients display signs of speech (dysarthria) and swallowing (dysphagia) difficulties with
excessive saliva production (sialorrhoea) that is often accompanied by shoulder and neck
weakness and noticeable emotional changes such as labile affect (Hillel & Miller, 1989; Mitchell
& Borasio, 2007). Spinal-onset patients display initial LMN loss in various regions of the spinal
cord and present with proximal or distal weakness of the limbs, which usually manifests as
difficulty climbing stairs, weakened grasp, or foot drop (Hillel & Miller, 1989; Mitchell & Borasio,
2007). In comparison to spinal-onset, bulbar-onset is associated with a worse prognosis (A. D.
Rosen, 1978) and more severe cognitive dysfunction (see Section 1.3).
1.2 Etiology
The etiology of ALS is uncertain, although hypotheses of viral infections (Mitchell & Borasio,
2007), toxic heavy-metals (Haverkamp et al., 1995; Mitchell, 1987), lifestyle-related risk factors
(such as poor diet or smoking) (Armon, 2003), and oxidative stress leading to excitotoxic motor
neuron death (for review see: (Carrì, Ferri, Cozzolino, Calabrese, & Rotilio, 2003)) have
previously been investigated. ALS is a rare disease with an incidence between 1.5 and 2.0 per
100,000 people per year, and a prevalence of 6 per 100,000 people (Mitchell & Borasio, 2007).
Of these cases, it is estimated that 10% are genetically inherited (‘familial ALS’), while the
remaining 90% of cases are ‘sporadic’ (Kwiatkowski et al., 2009; Mitchell & Borasio, 2007;
Phukan et al., 2007). In familial ALS, the Mendelian pattern of inheritance is most often
autosomal-dominant (Mitchell & Borasio, 2007), although instances of autosomal-recessive and
x-linked inheritance have also been noted (Figlewicz & Orrell, 2003).
Pathological reviews suggest that ALS is often associated with aggregations of
neurofilaments and misfolded proteins which induce a cascade of oxidative stress and
4
excitotoxic motor neuron death (Brown Jr, 1995; Carrì et al., 2003). Of the identified causative
genes in ALS, the superoxide dismutase 1 (SOD-1) locus on chromosome 21 accounts for
approximately 20% of familial ALS cases (D. R. Rosen et al., 1993). SOD-1 is a potent antioxidant
that is expressed in virtually every cell type, but in mutant forms increases neurotoxicity through
mechanisms that are not yet understood (Banci et al., 2008; Brown Jr, 1995; Carrì et al., 2003).
Another suspected cause of ALS is overproduction of the nervous system’s primary
excitatory neurotransmitter: glutamate. Glutamate receptors are responsible for allowing large
amounts of calcium (Ca2+) to enter the cell, which is affiliated with excitotoxic cell death (Choi,
1992). Neurotoxic effects related to activation of glutamate receptors were documented in rat
motor neurons that had been cultured in cerebrospinal fluid (CSF) recovered from ALS patients
(Couratier et al., 1993). This hypothesis was further supported by findings that chronic increases
in glutamate via blocked glutamate transport produced motor neuron toxicity in spinal cord
slices (Rothstein, Jin, Dykes-Hoberg, & Kuncl, 1993). Riluzole
is a glutamate-release inhibitor
that has been clinically proven to slow the progression of ALS; it remains the only Food and Drug
Administration (FDA) approved and efficacious therapy for ALS (Miller, Mitchell, Lyon, & Moore,
2007).
The aggregation of misfolded protein inclusions is a hallmark of neurodegenerative
pathology (Forman, Trojanowski, & Lee, 2004). Ubiquitin-positive and tau-negative inclusions
have been found in the cytoplasm of LMNs (Arai et al., 2006; Mackenzie, A H, & Feldman, 2005;
Yoshida, 2004) and in cortical and hippocampal brain regions of patients with familial or
sporadic ALS (Bergmann, Kuchelmeister, Schmid, Kretzschmar, & Schröder, 1996) and
frontotemporal lobar degeneration (FTLD) (Jackson, Lennox, & Lowe, 1996). FTLD is a clinical
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syndrome affecting the frontal and temporal lobes and is characterized by progressive dementia
producing cognitive, language and behavioural changes. FTLD encompasses three sub-clinical
syndromes (see Neary Criteria: (Neary et al., 1998; Neary et al., 2000)), most notably
frontotemporal dementia (FTD), and is widely believed to represent a spectrum of disease
sharing a common pathological entity with ALS (Arai et al., 2006; Bergmann et al., 1996; Jackson
et al., 1996; Mackenzie et al., 2005; Neary et al., 1998; Strong et al., 1999; Wilson, Grace,
Munoz, He, & Strong, 2001; Yang, Sopper, LeystraLantz, & Strong, 2003; Yoshida, 2004). This
hypothesis of an ALS – FTLD continuum was further confirmed upon discovery of TAR-DNAbinding protein 43 (TDP-43), a component of ubiquitin-positive tau-negative inclusions, as the
major disease protein in both ALS and FTLD (Neumann et al., 2006). TDP-43 is widely expressed
in heart, lung, muscle, and brain tissue (Buratti et al., 2001), and is normally restricted to the
nucleus but builds up in the cytoplasm during pathogenesis (Neumann et al., 2006).
Another mutation that is commonly linked to familial ALS is the fused in sarcoma/translated
in liposarcoma (FUS/TLS) gene on chromosome 16 (Kwiatkowski et al., 2009). FUS/TLS proteins
are responsible for diverse functions including RNA binding, and follow a similar pathological
mechanism as TDP-43 (Kwiatkowski et al., 2009). The numerous documented protein alterations
involved with ALS pathology (some of which were not discussed here in the interest of brevity),
and the support of a common biological substrate between ALS and FTLD suggests that the
cause of this disease is likely dependent on complex interactions between genetic predisposition
and environmental factors.
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1.3 Cognitive Impairment in ALS
Previously, changes in cognitive function had only been detected in a subgroup of 3% to 5%
of ALS patients (Kew & Leigh, 1992; Neary & Snowden, 1996); however, more recent studies
have identified some degree of cognitive impairment and/or behavioural disturbance in 30% to
50% of patients through neuropsychological assessments (Lomen-Hoerth et al., 2003; Massman
et al., 1996; Ringholz et al., 2005). The cognitive impairments found in ALS patients are usually
attributed to changes in frontal lobe, or ‘executive’ functions that include, but are not limited to,
mental flexibility (Strong et al., 1999), abstract reasoning and problem solving (Flaherty-Craig,
Eslinger, Stephens, & Simmons, 2006; Kew et al., 1993a), attention and concentration (Frank,
Haas, Heinze, Stark, & Münte, 1997; Massman et al., 1996; Ringholz et al., 2005), and internally
guided responses such as verbal fluency (Abrahams et al., 2000; Abrahams et al., 2005;
Abrahams, Leigh, & Goldstein, 2005; Evdokimidis et al., 2002; Frank et al., 1997; Ludolph et al.,
1992; Massman et al., 1996; Murphy et al., 2007b). In addition to frontal lobe dysfunction,
subtle deterioration of working memory (Abrahams et al., 1996; Abrahams et al., 1997;
Abrahams et al., 2000; David & Gillhamm, 1986; Kew et al., 1993a; Neary et al., 1990; Ringholz
et al., 2005; Strong et al., 1999), and occasional reports of language deficits (Neary et al., 1990)
which worsen over time (Abrahams et al., 2005) have also been documented. The behavioural
changes noted most often in ALS include apathy, irritability and aggression, loss of insight, social
disinhibition, impulsivity, poor judgement, emotional lability (dramatic mood fluctuations),
compulsiveness and rigidity of thinking (Merrilees, Klapper, Murphy, Lomen-Hoerth, & Miller,
2010; Phukan et al., 2007; Woolley & Katz, 2008).
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1.3.1 Evidence of an ALS-FTLD continuum
FTD, the most common behavioural subtype of FTLD, is characterized by the Neary Criteria
as a gradual decline in social and personal conduct accompanied by emotional blunting, change
in character, and loss of insight (Neary et al., 1990; Neary & Snowden, 1996). These symptoms
overlap substantially with cognitive and behavioural impairments seen in ALS, which further
supports the hypothesis of a biological disease continuum. It is estimated that 15-25% of ALS
patients have definite FTLD, although it is believed that up to 50% of patients display FTD-like
deficits without meeting all criteria for official diagnosis (Lomen-Hoerth et al., 2003; Ringholz et
al., 2005). In addition to the pathological (TDP-43) and behavioural evidence of an ALS and FTLD
continuum, neuroimaging techniques have shown that the degree and pattern of degeneration
in extramotor brain areas in ALS draws close similarities to that in FTD (Abrahams et al., 1996;
Kew et al., 1993b). Furthermore, the degeneration of extramotor brain areas has been shown to
increase with the degree of cognitive impairment (Ringholz et al., 2005; Wilson et al., 2001; Yang
et al., 2003). This suggests that neuropsychological tests are useful for identifying cognitive and
behavioural changes that likely result from frontotemporal pathogenesis, and may be extremely
valuable in providing insight to the extramotor neurological changes that occur in ALS.
Cognitive impairments also exist in a number of other neurodegenerative diseases
(Alzheimer’s, Parkinson’s, and progressive supranuclear palsy), so it is important to study the
nature of impairment specific to ALS, in order to distinguish how ALS affects brain function in
relation to other neurodegenerative diseases. The pattern of extramotor degeneration and
resulting dementia seen in ALS with or without memory loss is pathologically and
psychometrically distinct from the dementia associated with Alzheimer’s disease (Neary et al.,
8
1990; Takeda, Uchihara, Arai, Mizutani, & Iwata, 2009). However, it is not uncommon for
patients who display dementia to be initially diagnosed with Alzheimer’s, and then switched to a
diagnosis of ALS/FTD when they start to display motor symptoms, which can often appear after
dementia begins (Heidler‐Gary & Hillis, 2007).
The correct identification of FTD-like impairments in ALS patients through
neuropsychological evaluation may be additionally beneficial since recent evidence found
ALS/FTD comorbidity was associated with an adverse effect on survival in bulbar patients, more
so than a diagnosis of ALS or FTD alone (Neary et al., 2000; Olney et al., 2005). It should be
noted that while this adverse effect was independent from the patient’s age at bulbar onset, it
was not independent from the type of onset (bulbar or spinal) (Olney et al., 2005). These
findings must therefore be interpreted with caution, since bulbar-onset is associated with a
worse prognosis (Neary et al., 2000; A. D. Rosen, 1978) and more profound executive
dysfunction (Abrahams et al., 1997; David & Gillhamm, 1986; Lomen-Hoerth et al., 2003;
Massman et al., 1996). Further caution must be applied considering that bulbar-onset occurs
more frequently in patients with ALS/FTD (Neary et al., 2000; Olney et al., 2005). Meanwhile an
absence of bulbar involvement does not imply an absence of cognitive impairment (Strong et al.,
1999). In contrast, others have found no difference between bulbar- and spinal-onset ALS
patients on tests of executive function at the initial test date (Frank et al., 1997; Ringholz et al.,
2005; Strong et al., 1999), but found progressive frontal dysfunction in bulbar patients when
tests were re-administered during follow-up evaluations (Strong et al., 1999).
Despite their usefulness in identifying FTD-like symptoms, the executive dysfunction and
behavioural deficits seen in ALS are not exclusive to patients with bulbar-onset or ALS/FTD
9
comorbidity; deficits have also been reported, to a degree, in ‘non-demented’ ALS patients
(Abrahams et al., 1997; Abrahams et al., 2005; David & Gillhamm, 1986; Talbot et al., 1995).
Neuropsychological evaluations can therefore be employed not only to identify impairments in
non-demented ALS patients, but can also play a key role in the early identification of dementia
in ALS (Strong et al., 1999). However, some warn that mild and/or early dementia can
sometimes present without any cognitive impairments; it has been recommended that tests
sensitive to behavioural deficits be employed as well, in order to better detect more subtle
forms of dementia that affect behaviour before cognition (Neary et al., 2000). Additionally,
others advise that detection of dementia should rely on neuropsychological assessments in
conjunction with pathological or neuroimaging evidence for the best predictive outcomes
(Barson, Kinsella, Ong, & Mathers, 2000).
1.3.2 The importance of neuropsychological testing
Neuropsychological evaluations may also provide important insight into the degree of
pathological involvement and the ensuing physical impairment in ALS; research has shown that
the degree of cognitive impairment is correlated with greater motor deficits (Massman et al.,
1996), the presence of dysarthria (although some non-dysarthric patients were also impaired)
(Massman et al., 1996), and pseudobulbar palsy (a combination of dysarthria, dysphagia, and
emotional lability signs) (Abrahams et al., 1997; David & Gillhamm, 1986; Lomen-Hoerth et al.,
2003; Massman et al., 1996).
One of the most strikingly consistent reports of executive dysfunction in ALS has been
shown through measures of verbal fluency. Verbal fluency reliably reflects executive function
because it requires the generation of internally-guided responses and preserved working
10
memory, but is independent of primary linguistic abilities (Abrahams et al., 2000). ALS patients
invariably demonstrate verbal fluency deficits in comparison to normative data (Abrahams et al.,
2000; Frank et al., 1997; Kew et al., 1993a; Strong et al., 1999), with deficits even more marked
in bulbar-onset patients (Strong et al., 1999). Additionally, verbal fluency is sensitive to subtle
reductions in cognition that manifest early on in the disease, and these deficits remain
consistent over time even in non-demented ALS (Abrahams et al., 2005). Furthermore, ALS
patients who demonstrate verbal fluency impairment display decreased regional cerebral blood
flow (rCBF) in the prefrontal cortices compared those who were unimpaired (Kew et al., 1993a).
Despite the increased use of neuropsychological tests to evaluate cognitive impairment in
ALS, the degree and pattern of progression is still not well understood. A high level of attrition
due to the rapidly progressive nature of ALS makes studying cognitive and behavioural changes
over time within a cohort of patients extremely challenging. However, early administration of
such tests may be vital for the early diagnosis of FTD in ALS which may precede, follow, or
coincide with the onset of motor symptoms (Mackenzie et al., 2005) and is linked to adverse
effects on survival (Olney et al., 2005). A recent study found that in 58% of ALS/FTD patients, the
onset of FTD symptoms occurred approximately 3 years before the onset of ALS symptoms,
while 38% had ALS/FTD concurrent onset (Guedj et al., 2007). A prospective study found that
ALS patients who met the FTD criteria showed cognitive and behavioural decline, on average,
more than 7 years before the onset of motor symptoms (Murphy et al., 2007b). Thus, it is useful
to develop robust tools for identifying the FTD-like cognitive deficits in ALS, as FTD symptoms
may provide an earlier marker for ALS diagnosis.
11
Considering the validity of neuropsychological tests in predicting the onset or severity of
cognitive decline in ALS, the current study evaluated the degree of behavioural and cognitive
impairment in ALS using measures of verbal fluency, working memory, attention, abstract
reasoning, signs of FTD comorbidity, and emotional lability (see Methods for details). The
current study also included evaluations of functional and physical abilities to assess the degree
of progression on an individual basis. Anxiety and depression measures were taken to eliminate
the possibility of the patient’s emotional state influencing their cognitive profile (see Methods).
1.4 Saccadic Eye Movement System
Saccades are rapid eye movements used to direct the fovea to an object or location of
interest. Saccades offer a popular model for studying cognitive function and flexible control of
behaviour because: 1) they can be triggered automatically towards novel stimuli as part of the
‘visual grasp’ reflex (Hess, Burgi, & Bucher, 1946), or suppressed when inappropriate to instead
be elicited voluntarily towards a desired location; 2) they are easily implemented in clinical- or
laboratory-based settings; 3) their properties and underlying brain areas are well understood;
and 4) saccadic control is often impaired in a variety of clinical groups.
In the prosaccade task, subjects are instructed to look towards a peripheral target (T)
immediately after it is presented, which requires an automatic saccade response that is often
referred to as ‘visually-guided’ (Fig. 1.1A). In the prosaccade task, the target location and the
saccade goal are compatible and therefore require a direct sensorimotor transformation (Munoz
& Everling, 2004). In the antisaccade task (Hallett, 1978; Munoz & Everling, 2004), subjects are
instructed to look away from T to its mirrored position. In order to do this, the subject must
suppress the automatic response to instead make a voluntary saccade in the opposite direction
12
of T (Fig. 1.1A). In this task, the target location and the saccade goal are incompatible, which
requires a more difficult sensorimotor transformation of vector inversion (Munoz & Everling,
2004). The prosaccade and antisaccade instructions are conveyed to the subject by the colour of
the fixation point (FP) (see Methods for details). Sometimes subjects make direction errors (i.e.
erroneous prosaccade on an antisaccade trial), which they are encouraged to correct
immediately in order to convey that they understood the task, but just had difficulty suppressing
the automatic response (Fig. 1.1B).
Figure 1.1: Schematic of prosaccade and antisaccade tasks. (A) Subject is instructed to look
from the fixation point (FP) either towards (prosaccade) or away from (antisaccade) the target
(T) based on the colour of FP. Failure to suppress the automatic response results in a direction
error (dashed arrow). (B) Example of eye position traces for the antisaccade task showing
correct responses (solid line) and corrected direction errors (dashed line). FP disappears 200 ms
before T appears, creating a gap. Latency of response is shown as saccadic reaction time (SRT).
Figures adapted with permission from Munoz et al. (2007).
13
1.4.1 The oculomotor circuit
The oculomotor circuit is highly complex; several brain regions and multiple neuronal
populations contribute to the control and production of saccades (Fig. 1.2). Brain regions
including areas in the frontal and parietal cortices, the superior colliculus (SC), basal ganglia
(BG), thalamus, paramedian pontine reticular formation (PPRF), and cerebellum have been
identified as neural correlates of saccadic control through anatomical, radiological, physiological
and clinical studies (Hikosaka, Takikawa, & Kawagoe, 2000; Hikosaka, Nakamura, & Nakahara,
2006; Leigh & Kennard, 2004; McDowell, Dyckman, Austin, & Clementz, 2008; Moschovakis,
Scudder, & Highstein, 1996; Munoz & Everling, 2004; Pierrot-Deseilligny, Ploner, Muri, Gaymard,
& Rivaud-Pechoux, 2002; Scudder, Kaneko, & Fuchs, 2002; Wurtz, Sommer, Paré, & Ferraina,
2001).
14
Frontal Cortex
SEF
DLPFC
Anterior
Thalamus
SNr
Posterior
Thalamus
SCi SCs
Basal Ganglia
Cerebellum
LGN
Retina
retinotectal
pathway
retino-geniculo-cortical
pathway
direct
pathway
indirect
pathway
PEF
Visual Cortex
CD
STN
Parietal Cortex
FEF
hyperdirect
pathway
GPe
Inhibitory
Excitatory
Eye Muscles
PPRF
CD, caudate nucleus; DLPFC, dorsolateral prefrontal cortex; FEF, frontal eye
fields; GPe, external segment of globus pallidus; LGN, lateral geniculate nucleus;
PEF, parietal eye fields; PPRF, paramedian pontine reticular formation; SCi,
intermediate layers of superior colliculus; SCs, superficial layers of SC; SEF,
supplementary eye fields; SNr; substantia nigra pars reticulata; STN; subthalamic
nucleus
Figure 1.2: Schematic of the oculomotor circuit (regions of interest for this project are
highlighted in orange). Figure adapted with permission from Munoz et al. (2007).
Visual information enters the oculomotor circuit through the retina to the lateral geniculate
nucleus (LGN) of the thalamus where it is relayed to the visual cortex via the retino-geniculocortical pathway. Projections from the visual cortex extend directly to the superficial and
intermediate layers of the SC (SCs/SCi) and carry information required for producing automatic
saccades (C. E. Collins, Lyon, & Kaas, 2005; Schiller, Stryker, Cynader, & Berman, 1974).
Information from the visual cortex can also be carried, via the dorsal stream, to motor areas
within the parietal lobes, such as the parietal eye fields (PEF) (Greenlee, 1999). The parietal
15
cortex transforms sensory coordinates into motor coordinates (Colby & Goldberg, 1999) and
projects to the SCi (Paré & Wurtz, 2001), which is organized retinotopically to produce saccades
to a specific location in space. The parietal cortex appears to be important for initiating
automatic saccades, since lesions to the PEF significantly increases prosaccade latencies (PierrotDeseilligny, Rivaud, Gaymard, & Agid, 1991a). The PEF also act as an interface with oculomotor
areas in the frontal cortex including the frontal eye fields (FEF), supplementary eye fields (SEF)
and the dorsolateral prefrontal cortex (DLPFC) (Connolly, Goodale, Menon, & Munoz, 2002;
DeSouza, Menon, & Everling, 2003; Pierrot-Deseilligny et al., 2002).
Lesions of the DLPFC result in higher percentage of direction errors made on the antisaccade
task (Pierrot-Deseilligny et al., 1991a; Pierrot-Deseilligny et al., 2003), which suggests that DLPFC
is responsible for the suppression of unwanted automatic saccades. In contrast, lesions to FEF
cause a normal percentage of errors but result in longer antisaccade saccadic reaction times
(SRTs) (Pierrot-Deseilligny et al., 2003; Rivaud, Müri, Gaymard, Vermersch, & Pierrot-Deseilligny,
1994), suggesting that FEF is more involved in producing correct antisaccades. SRT is a byproduct of the time required to process visual information, choose a target and program a
motor response, and varies depending on contrast, luminance, or the cognitive difficulty of the
task (Leigh & Kennard, 2004). Lesion and neural recording studies reveal that SEF is involved
with sequencing multiple saccades and the associated intrinsic decision-making process (Coe,
Tomihara, Matsuzawa, & Hikosaka, 2002; Gaymard, Rivaud, & Pierrot-Deseilligny, 1993).
The FEF, SEF and DLPFC all project to the SCi to mediate voluntary saccade control, either
through direct projections or indirectly through the primary input area of the BG, the caudate
nucleus (CD) (Everling & Munoz, 2000; Huerta, Krubitzer, & Kaas, 1986; Huerta & Kaas, 1990).
16
Neural activity in CD has been correlated with automatic and voluntary saccade behaviour
(Hikosaka et al., 2000), which can be modulated by reward (Hikosaka et al., 2006). In particular,
the CD is presumably critically involved with online control of initiating antisaccades (Watanabe
& Munoz, 2009). The BG has a number of pathways, the direct, indirect, and hyperdirect
pathways (Fig 1.2), which are separately involved in triggering, suppressing or rapidly halting
automatic responses through the nature of their connections with SC, respectively (Hikosaka et
al., 2000). Inputs from these BG pathways and direct inputs from frontal and parietal cortices
must be integrated by SC to produce eye movements (Hikosaka et al., 2000; Hikosaka et al.,
2006).
Upon integrating the various inputs of descending projections from visual, parietal and
frontal cortices and the BG, the SCi acts as a critical interface by sending motor commands to
the ‘saccade generator’ in the brainstem: the PPRF. The PPRF is involved with sending motor
commands to the six extraocular muscles, which ultimately produce the programmed saccade.
PPRF is known to incorporate inputs from the SCi and cerebellum to monitor the accuracy of the
eye movement using online visual feedback acquired during the execution of a saccade.
1.4.2 Express saccades
Automatic, visually-guided saccades are often so rapid that they occur before any cognitive
processing has taken place; therefore their timeliness is determined by the amount of pre-target
(before presentation of peripheral T) neural activity in the SC (Everling, Dorris, & Munoz, 1998;
Leigh & Kennard, 2004). High levels of pre-target activity within the SC combine with the visual
response to produce the most rapid automatic saccade: the ‘express’ saccade (Fischer & Boch,
1983; Fischer & Weber, 1993; Paré & Munoz, 1996). Typical express saccade latencies span 90 –
17
140 ms (Dorris, Pare, & Munoz, 1997; Fischer & Boch, 1983; Fischer & Weber, 1993; Munoz,
Broughton, Goldring, & Armstrong, 1998) and are close to the minimum conduction time for a
simple afferent and efferent command (Carpenter, 1981); in other words, the minimum time for
a visual response to be converted into a motor command. Express saccades represent the visual
response taking the most direct route from the visual cortex, through SCi, to the brainstem
saccade generator where the motor command is executed (Dorris et al., 1997; Schiller, Sandell,
& Maunsell, 1987).
Express saccades can occur when subjects have been instructed to execute a prosaccade,
and there has been enough pre-target neural activity in SC to surpass the saccade threshold
upon the occurrence of a visual response. The likelihood of an express saccade being generated
is increased when the FP is removed before the presentation of the peripheral T to create a
‘gap’ condition (usually ≈ 200 ms) where the subject sees a black screen and receives no visual
input (Fig. 1.1B) (Dorris & Munoz, 1995). The gap condition facilitates express saccades by
disengaging fixation neurons to reduce the inhibition of the retinotopic saccade map in SC,
thereby allowing pre-target activity to build up so that when the visual response occurs, the
threshold is readily surpassed to evoke an express saccade (Dorris & Munoz, 1995; Dorris et al.,
1997; Fischer & Ramsperger, 1984; Fischer & Weber, 1997; Paré & Munoz, 1996). The SRTs for
the gap condition tend to resemble a bimodal distribution, where the first peak of reaction
times fall around 100 ms, making them ‘express’ saccades, while the second peak falls around
150 ms, making them ‘regular’ latency automatic saccades (Fischer & Boch, 1983; Schiller et al.,
1987). Lesions to SC cause attenuation of express saccade production, suggesting express
18
saccades require an intact SC (Pierrot-Deseilligny, Rosa, Masmoudi, Rivaud, & Gaymard, 1991b;
Schiller et al., 1987).
1.4.3 Preparatory set
The successful production of antisaccades depends upon executive control and the ability to
suppress the visual grasp reflex, a phenomenon where visual input is directly transformed into a
motor command within the SC to produce an orienting saccade (Munoz & Everling, 2004). In
particular, the success of forthcoming saccades is often dictated by the amount of pre-target
neural activity in the SC (Everling et al., 1998; Everling & Munoz, 2000) where lower levels of
activity are associated with correct performance of the antisaccade task (Everling, Dorris, Klein,
& Munoz, 1999). The amount of pre-target activity is also modulated by the likelihood of the
target being presented in a particular location in the visual field (Dorris & Munoz, 1998; Paré &
Munoz, 1996), with greater neural activity building up in areas that are predicted to display the
visual response. This phenomenon is known as ‘preparatory set’ and refers to the taskappropriate amount of build-up neural activity in the SC, and is important for preparing the
desired motor response (for example, lower levels of activity would be more desirable for
producing correct antisaccades, while higher levels would be more desirable for producing fast
express saccades).
Preparatory set in the SC can be influenced by other brain areas that project to it,
particularly those involved with executive function; the frontal lobes are largely responsible for
producing adequate inhibition of the saccade map to reduce pre-target activity in the SC and
prevent erroneous prosaccades on an antisaccade task (Everling et al., 1998; Everling et al.,
1999; Everling & Munoz, 2000). Inhibitory signals evoked in the frontal lobes and BG might
19
explain the higher levels of blood oxygen-level dependent (BOLD) signal that have been
consistently reported in FEF, SEF, PEF (Brown, Vilis, & Everling, 2007; Connolly, Goodale, Goltz, &
Munoz, 2005; Connolly, Goodale, Cant, & Munoz, 2007; Curtis & D'Esposito, 2003b; Everling &
Munoz, 2000; Schlag-Rey & Amador, 1997), DLPFC (Guitton, Buchtel, & Douglas, 1985; PierrotDeseilligny et al., 1991a), and CD (Watanabe & Munoz, 2009) when preparing for an antisaccade
compared to a prosaccade. Pre-target activity in FEF is particularly important in predicting the
resulting motor response, as it can influence the rate of antisaccade errors, percentage of
express saccades, and can influence SRT (Connolly et al., 2002; Everling & Munoz, 2000).
Therefore, the current investigation will focus on FEF, SEF, PEF, DLPFC, and CD as regions of
interest (ROIs) (Fig. 1.2) for analysis of the functional magnetic resonance imaging (fMRI) data
collected due to their projections to SC, their involvement with antisaccade preparatory set, and
their ability to influence response times.
1.4.4 Eye movements in ALS
Saccades may function as a diagnostic tool for identifying neurological disorders (Leigh &
Kennard, 2004), since eye movement deficits have been well documented in a number of clinical
populations (Cameron, Watanabe, Pari, & Munoz, 2010; Chan, Armstrong, Pari, Riopelle, &
Munoz, 2005; Green, Munoz, Nikkel, & Reynolds, 2007; Meienberg, Muri, & Rabineau, 1986;
Munoz, Armstrong, Hampton, & Moore, 2003; Munoz, Armstrong, & Coe, 2007; Peltsch,
Hoffman, Armstrong, Pari, & Munoz, 2008; Perneczky et al., 2011; Reuter & Kathmann, 2004;
Serra, Derwenskus, Downey, & Leigh, 2003). Recent findings have shown promise for using
saccades to distinguish ALS from its mimic disorders (Donaghy, Thurtell, Pioro, & Leigh, 2010b;
Donaghy, Thurtell, Pioro, Gibson, & Leigh, 2011) because saccade properties will often deviate
20
when lesions overlap with oculomotor brain regions (Pierrot-Deseilligny, et al., 1991b; PierrotDeseilligny et al., 2003; Rivaud et al., 1994). Atrophy of the frontal lobes has been noted in ALS
patients with FTD-like cognitive changes, where more widespread atrophy was associated with
more substantial executive dysfunction and behavioural deficits (Abrahams et al., 1996;
Abrahams et al., 2000; Guedj et al., 2007; Murphy et al., 2007b; Neary et al., 2000; Yoshida,
2004). In addition to being responsible for executive processes such as inhibition, the frontal
lobes house oculomotor control areas such as the FEF and DLPFC, which are required to
correctly perform the antisaccade task (Pierrot-Deseilligny et al., 2003; Rivaud et al., 1994).
Arguably, if a proportion of ALS patients demonstrated widespread atrophy of the frontal lobes
as a result of an ALS/FTD-related pathology, this would be expected to have implications on
their ability to inhibit unwanted prosaccades in order to correctly perform an antisaccade.
Saccades also provide a unique opportunity to study one of the last functioning voluntary
motor circuits in ALS (Leveille, Kiernan, Goodwin, & Antel, 1982; Marti-Fàbregas & Roig, 1993).
The machinery required for producing an eye movement remains intact even in the late stages
of ALS; motor neurons from the trochlear and oculomotor nuclei control most of the extraocular
muscles and maintain their integrity according to pathological findings (Okamoto et al., 1993).
Only when patients had been on end-of-life respiratory support did they sometimes develop
ophthalmoplegia, a weakness of the extraocular muscles caused by damage to their nuclei, and
therefore displayed saccadic abnormalities in terms of saccade latency and range of motion
(Averbuch-Heller, Helmchen, Horn, Leigh, & Büttner-Ennever, 1998; Kato, Hayashi, & Yagishita,
1993). Therefore, it is reasonable to assume that if eye movement abnormalities are detected in
ALS patients near the beginning of their prognosis, it is likely a result of upstream damage within
21
oculomotor cortical control areas, rather than a result of downstream deterioration of the
trochlear and oculomotor nuclei causing ophthalmoplegia.
While there have only been a handful of studies evaluating eye movements in ALS, they
have consistently demonstrated saccade deficits (Averbuch-Heller et al., 1998; Donaghy et al.,
2010a; Donaghy et al., 2011; Evdokimidis et al., 2002; Leveille et al., 1982; Marti-Fàbregas &
Roig, 1993; Ohki et al., 1994; Shaunak et al., 1995). Specifically, ALS patients generated more
direction errors and had longer latencies for antisaccades than healthy controls (Donaghy et al.,
2010a; Donaghy et al., 2010b; Shaunak et al., 1995); meanwhile, no abnormalities were
documented for prosaccades (Shaunak et al., 1995). These impairments point to FEF and DLPFC
inadequately suppressing the visual grasp reflex, and might be an indication of frontal lobe
pathology in ALS.
There has been some evidence that different locations of symptom onset in ALS may
influence SRTs; in particular, bulbar-onset patients tended to have longer prosaccade latencies
than spinal-onset and controls (Donaghy et al., 2010a). Longer latencies were also noted during
delayed and remembered saccade tasks and were partly attributed to a difficulty suppressing
unwanted saccades, despite saccade accuracy being unaffected (Evdokimidis et al., 2002). In the
same study, the percentage of direction errors on the antisaccade task correlated with the
degree of frontal lobe impairment and occurred more often in patients displaying bulbar
symptoms such as dysarthria (Evdokimidis et al., 2002). Pseudobulbar palsy (which includes
dysarthria) in ALS has previously been associated with executive dysfunction (Abrahams et al.,
1997), and likely explains the greater difficulty for dysarthric patients to inhibit unwanted
saccades. The greater saccade impairments seen in bulbar ALS patients might be attributed to a
22
more advanced deterioration of cortical areas involved with saccade control, or perhaps is a
result of more extensive pathological involvement of the brainstem.
Saccadic intrusions are involuntary conjugated eye movements that occur naturally during
fixation in healthy adults, and are usually characterized by an initial fast saccade away from the
FP that is followed by a slightly delayed second saccade or drift back to the fixation location
(Abadi & Gowen, 2004). Saccadic intrusions have also been reported in ALS patients (Donaghy et
al., 2009; Marti-Fàbregas & Roig, 1993); however, the conditions under which these intrusions
occur are conflicting. Intrusions happened significantly more in ALS patients than controls when
fixating on a central FP (Shaunak et al., 1995); while others only found this when no FP was
present (Donaghy et al., 2009). Additionally, some have reported an association between the
frequency of intrusions and bulbar symptoms (Marti-Fàbregas & Roig, 1993), while others found
this association to be stronger in spinal-onset patients (Donaghy et al., 2009). Despite this
controversy, it is generally accepted that intrusions during fixation occur more often in ALS
patients with impaired frontal lobe functions (Donaghy et al., 2009; Shaunak et al., 1995) and
might be attributed to attentional shifts during fixation (Abadi & Gowen, 2004).
In summary, the saccadic behaviour previously documented in ALS patients such as
increased antisaccade latencies, greater antisaccade direction errors, and more saccadic
intrusions, all appear to occur more frequently in those patients with impaired executive control
and likely points toward ALS/FTD pathology. Therefore, measures of saccadic performance can
provide important insight into the degree of cognitive impairment in ALS patients.
23
1.5 Neuroimaging in ALS
While eye movements and neuropsychological tests have previously been employed
simultaneously in ALS research, it is believed that no other study has combined these
techniques with neuroimaging to provide a more holistic approach to studying the neural
correlates of cognitive impairment in ALS. There have been a number of neuroimaging
techniques employed by others to explore the structural and functional changes that occur in
the ALS brain, ranging from magnetic resonance imaging (MRI), to single-photon emission
computed tomography (SPECT), to computed tomography (CT), to positron emission
tomography (PET), each with their own advantages and disadvantages. The current investigation
used fMRI to study the degree of brain activation (in terms of BOLD signal) in combination with
detailed structural images acquired using an MRI to evaluate changes in cortical grey matter.
There has been substantial evidence pointing to abnormalities in brain regions that lie
outside of the motor cortex in ALS (Abrahams et al., 1996; Abrahams et al., 2000; Abrahams et
al., 2005; Chang et al., 2005; Guedj et al., 2007; Neary et al., 2000). ALS patients tend to have
reduced perfusion (in terms of rCBF) in frontal, temporal, and parietal regions (Abrahams et al.,
1996; Guedj et al., 2007; Kew et al., 1993a; Kew et al., 1993b; Talbot et al., 1995) in addition to
lower overall cortical metabolism (in terms of nutrient uptake measured by SPECT), which tends
to be more marked in patients displaying cognitive impairment (Abe et al., 1997; Ludolph et al.,
1992; Neary et al., 1990; Talbot et al., 1995).
Functional differences have been reported previously in ALS patients using BOLD fMRI,
where reduced brain activation was found in premotor, supplementary motor and parietal
cortical areas (Tessitore et al., 2006). The structural changes that are most commonly noted in
24
ALS, with or without an abnormal cognitive profile, include neuronal loss in the motor cortex of
bulbar-onset patients, which also becomes evident later on in spinal-onset patients (Strong et
al., 1999; Strong, Kesavapany, & Pant, 2005). Additionally, enlarged ventricles (Frank et al.,
1997), and reductions in white (Abrahams et al., 2005) and grey matter (Chang et al., 2005;
Murphy et al., 2007b) within motor, premotor and frontotemporal regions were previously
described in ALS patients, although reductions were more substantial in those patients with
cognitive impairment. BOLD signal is predominantly measured from areas of grey matter;
therefore, if a reduction in grey matter within frontotemporal regions is characteristic of the
cortical degeneration in ALS, it may translate to changes in the level of brain activation in these
same areas.
White matter changes within the corticospinal tract are a classic pathological finding in ALS
(Hofmann et al., 1998; Neary et al., 2000; Thivard et al., 2007; Toosy, Werring, Orrell, & Howard,
2003). Studies using diffusion tensor imaging (DTI) to investigate the integrity of white matter
tracts in ALS have found reduced diffusion in the corticospinal tracts in multiple spinal segments
and in subcortical and cortical areas (Nair et al., 2010; Thivard et al., 2007; Toosy et al., 2003). A
change in the integrity of white matter tracts has clinical relevance and may underlie the
functional and cognitive impairment seen in ALS due to a lack of ‘connectedness’ between
subcortical areas. Interestingly, extramotor structural abnormalities have been noted even in
non-demented ALS patients, suggesting that structural changes may precede or perhaps be
responsible for the onset of cognitive abnormalities (Abrahams et al., 2003). This may have
valuable implications for using structural MRI as a biomarker for the early detection of the
25
pattern of cerebral involvement that is associated with the ALS/FTLD continuum (Murphy et al.,
2007b; Talbot et al., 1995).
1.6 Thesis Objectives
The main objective of this thesis is to gain new insights into the cognitive impairments seen
in ALS as the first study to combine eye movement recordings and neuropsychological
assessments with structural and functional MRI. I intend to capitalize on the overlap between
the oculomotor circuit and the brain regions that are pathologically altered in ALS to highlight
the neurological changes that occur in this disease. I hypothesize that a significant proportion of
ALS patients will have cognitive disturbances that will alter their flexible control over behaviour
and will be revealed in eye movement tasks designed to probe this flexibility. Behaviourally, I
expect to see task-related differences across prosaccades and antisaccades in measures of SRT,
coefficient of variation in SRT (CVSRT), and direction errors. More specifically, I predict slower
antisaccade SRTs and more antisaccade direction errors across ALS and control groups due to
the added cognitive processing required to perform the antisaccade task (prosaccade
suppression, attention redirection, saccade vector inversion) (DeSouza et al., 2003; Munoz &
Everling, 2004). This same task difference should apply to the functional data; I hypothesize that
antisaccades should elicit greater brain activation during saccade preparation than prosaccades
in terms of BOLD signal in the brain areas that are involved with executive function and
oculomotor control such as DLPFC and FEF (Pierrot-Deseilligny et al., 1991a; Pierrot-Deseilligny
et al., 2003) and in other oculomotor regions that modulate SC activity such as SEF, PEF, and CD.
I expect reductions in the amount of grey matter in ALS patients compared to controls, both
globally and specifically in frontotemporal regions and I predict this reduction in grey matter will
26
correspond to functional overcompensation during the saccade tasks. Ultimately, my aim in
combining these techniques is to identify the neural substrate for some of the cognitive
impairments in executive function that are present in ALS.
27
Chapter 2
Investigating cognitive impairments in amyotrophic lateral sclerosis using
saccadic eye movements and functional magnetic resonance imaging
2.1 Introduction
Amyotrophic lateral sclerosis (ALS) is a terminal neurodegenerative disease affecting the
upper and lower motor neurons that causes progressive weakness and eventual respiratory
failure. While ALS has traditionally been thought of as a motor disease, recent findings provide
evidence that up to 30-50% of ALS patients display some degree of cognitive or behavioural
impairment through neuropsychological examination (for review see: (Neary et al., 2000;
Phukan et al., 2007; Woolley & Katz, 2008)). Cognitive abilities associated with ‘executive
functions’ are most vulnerable to decline in ALS because the pattern of extramotor pathological
degeneration typically follows that of frontotemporal lobar degeneration (FTLD) and is thought
to share a common biological disease continuum (Abrahams et al., 1996; Kew et al., 1993b;
Lomen-Hoerth et al., 2003; Ringholz et al., 2005). In addition to executive processing
capabilities, the frontal lobes play a key role in the initiation of voluntary saccades and the
suppression of saccades to irrelevant visual stimuli (via their projections to the superior
colliculus (SC) (Everling et al., 1999; Munoz & Everling, 2004; Segraves & Goldberg, 1987) and
basal ganglia (BG) (Hikosaka et al., 2000; Hikosaka et al., 2006).
Saccades can help provide measures for identifying neurological disorders (Leigh & Kennard,
2004; Munoz & Everling, 2004; Munoz et al., 2007; Ramat, Leigh, Zee, & Optican, 2007), as
28
different saccade deficits have been documented in a number of clinical populations through
the use of various oculomotor tasks (Cameron et al., 2010; Chan et al., 2005; Green et al., 2007;
Meienberg et al., 1986; Munoz et al., 2003; Munoz et al., 2007; Peltsch et al., 2008; Perneczky et
al., 2011; Reuter & Kathmann, 2004; Serra et al., 2003). More specific to ALS, saccades provide a
valuable tool for probing the variable cognitive deficits that have been consistently reported in
the literature that closely resemble deficits of frontotemporal dementia (FTD), a sub-type of
FTLD (for review see: (Sharma et al., 2011)). Finally, the oculomotor network provides the
unique opportunity to study one of the last functioning voluntary motor circuits in ALS, due to
the progressive loss of motor control (Leveille et al., 1982; Marti-Fàbregas & Roig, 1993).
The ‘prosaccade’ task (Fig. 1.1A) requires subjects to look towards a peripheral visual target
when it appears, and provides a measure of their ability to generate automatic, visuallytriggered saccades. Visual input travels from the retina to the visual cortex, where it can extend
directly to the superior colliculus (SC) (C. E. Collins et al., 2005; Schiller et al., 1974) or to parietal
areas via the dorsal stream to guide an automatic saccade (Greenlee, 1999). ‘Express’ saccades
are elicited on prosaccade trials when pre-target neural activity in SC is sufficient to surpass the
saccade threshold upon the occurrence of a visual response. In an express saccade, the visual
response takes a direct route from the visual cortex, through the SC, to the brainstem saccade
generator (Dorris et al., 1997; Schiller et al., 1987) where the motor command is executed.
The ‘antisaccade’ task (Fig. 1.1A) requires subjects to suppress the automatic saccade to
instead generate a saccade in the opposite direction of the target, providing a measure of
voluntary control (Hallett, 1978; Munoz & Everling, 2004). A properly executed antisaccade
requires intact frontal lobes, in particular the dorsolateral prefrontal cortex (DLPFC) and frontal
29
eye fields (FEF) which are responsible for suppression of unwanted saccades and initiation of
voluntary saccades (Guitton et al., 1985; Pierrot-Deseilligny et al., 1991a; Pierrot-Deseilligny et
al., 1991b; Pierrot-Deseilligny et al., 2003; Rivaud et al., 1994). Antisaccade deficits, including
increased occurrence of direction errors (erroneous prosaccades) and longer latencies, have
been reported in ALS (Donaghy et al., 2010a; Donaghy et al., 2010b; Shaunak et al., 1995), with a
relative absence of prosaccade deficits (Shaunak et al., 1995). These oculomotor impairments
may arise from the widespread pathological involvement of the frontal lobes in ALS, which is
often accompanied by executive dysfunction and behavioural deficits (Abrahams et al., 1996;
Abrahams et al., 2000; Guedj et al., 2007; Murphy et al., 2007b; Neary et al., 2000; Yoshida,
2004).‘Catch trials’ and ‘fixation trials’ were necessary for isolating preparation processes from
execution processes due to the nature of fMRI rapid event-related designs (Dale, 1999; Ollinger,
Shulman, & Corbetta, 2001) (see Section 2.2.7). Catch trials were identical to prosaccade and
antisaccade trials, however no peripheral target appeared.
To investigate the neural substrate of cognitive impairment and voluntary saccade control in
ALS, we combined neuropsychological evaluations, oculomotor tasks such as the prosaccade
and antisaccade, functional magnetic resonance imaging (fMRI) and structural MRI in group of
patients compared to age-matched controls to gain new insights into cognitive decline in ALS. To
our knowledge, this study marks the first to combine all of these techniques.
We hypothesize that a significant proportion of ALS patients will display cognitive
impairments that will alter their ability to exert flexible voluntary control over eye movement
behaviour. More specifically, we predict changes in the amount of brain activation (in terms of
blood oxygen-level dependent (BOLD) signal) in areas involved with executive function and
30
oculomotor control such as DLPFC and FEF, and in oculomotor regions that modulate SC activity
such as supplementary and parietal eye fields (SEF/PEF) and the caudate nucleus (CD). We
further hypothesize that structural analyses of cortical thickness will reveal decreased grey
matter in ALS patients compared to controls, both globally and specifically in frontotemporal
and oculomotor control regions, which will likely be more marked in patients displaying
cognitive impairment typical of FTD.
2.2 Methods
2.2.1 Subjects
All experimental procedures were approved by the Queen’s University Research and Ethics
Board, and complied with the principles of the Canadian Tri-council Policy Statement on Ethical
Conduct for Research Involving Humans and the principles of the Declaration of Helsinki (1964).
All subjects were recruited through the Queen’s University community or through the ALS clinics
at Kingston General Hospital and Saint Mary’s on the Lake Hospital, and gave their written and
informed consent. Twenty-one ALS patients (ages 44 – 89 years, 3 females, mean age = 63.3, SD
± 11.1) diagnosed by a neurologist (co-author M.M.) as having ‘definite’ or ‘probable’ ALS (See
Appendix A) (Brooks, 1994) were recruited for this study. Some ALS patients were unable to
complete all parts of this research protocol for various reasons; some patients were excluded
from the analysis based on their inability to complete the MRI session due to respiratory
compromise (N=2) (see Section 2.2.2), claustrophobia (N=1), MRI safety screening problems
(N=1), magnet trigger failure (N=1), complications with eye tracking (N=3), or head motion
exceeding 3 mm in any direction during scans (N=1) (Table 1).
31
Table 1: Clinical information for all recruited ALS patients
Patient Sex Age Edu. Mo.
Clinical Evaluation
Neuropsychological Evaluation
(yrs) (yrs) since El
FVC ALS MVIC ULT LLT Hand MoCA Cognistat COWA FBI CNSHADS
diagn. Escor %pred FRS-R (kg) (/70) (/70)
(/30)
(VF)
LS
R
J
D
A
1
2
3
4
5
6
7
8
9
10
11
12
13 (mt)
M
M
M
M
M
M
F
F
M
M
M
M
M
71
69
44
68
54
53
53
68
76
55
66
62
75
14
16
12
18
14
13
15
14
17
16
16
13
19
60
20
84
12
168
13
13
22
18
16
10
11
18
prob
def
def
def
def
def
def
def
def
def
def
def
def
78
90
92
101
63
100
62
78
88
74
77
94
92
38
41
29
41
42
31
28
36
42
43
31
33
39
14 (resp) F
15 (f)
M
55
64
12
10
def
def
81
84
38
12
16(resp)
17 (fs)
18 (clau)
19 (tr)
54
67
45
89
16
20
13
9
18
60
12
60
def
def
def
prob
38
55
70
91
70
71
8
9
12
12
prob
prob
73
95
M
M
M
M
20 (tr,nc) M
21 (mo) M
Mean 18M
(n=21) ; 3F
Mean 10M
(n=12) ; 2F
Controls 9M;
(n=12) 3F
63.3
5.5
45
31
18
6
3
18
30
3
50
3.3
48
50
70
38
64
67
56
50
70
69
53
64
56
70
58
42
70
62
64
63
60
43
60
68
26
65
61
R
L
L
L
R
R
25
25
24
21
30
23
29
25
18
29
23
26
26
7
8
4
4
8
4
8
8
7
7
8
8
8
6
6
5
4
5
5
6
6
5
6
5
5
5
3.37
4.70
7.00
12.10
2.96
14.80
2.93
4.14
3.50
4.88
9.11
3.83
10.13
42
2
24
12
2
11
10
13
5
3
4
4
4
10
7
11
11
19
7
15
11
9
7
9
13
10
0
1
9
3
2
1
5
3
3
1
3
4
1
5
0
9
2
5
5
5
2
9
1
7
1
8
45
57
70
69
50
R
23
15
5
3
4
4
n/a
18.20
8
24
18
0
4
25
21
31
33
19
3
0.1
18.6
60
37
25
63
29
34
63
57
R
R
R
R
25
24
18
24
6
7
5
6
6
6
3
5
4.08
9.80
9.94
7.62
13
24
9
10
15
16
14
21
10
4
0
2
29
36
20.6 65
10.6 47
66
96
L
R
13
22
4
4
4
5
14.08
19.30
14
0
13
5
0
R
R
L
R
R
R
14 32.6
79.81 34.4 19.9 57.2
57
23.24 6.14 5.05
8.32
11 12.42
61.6 14.8 37.3
83.08 36.3 19.3 58.9
57
24.83 6.75 5.33
6.11
11
62.6
32
11
completion
neuro
clinic psyc behav fMRI struct
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
n
y
y
y
y
n
n
n
n
n
n
y
14
(ax)
5
6
7
y
y
y
y
y
y
y
y
n
n
n
n
n
n
n
n
n
n
n
y
10
7
y
y
y
n
n
n
n
n
n
y
2.85 5.16
3
4
21
19
13
12
16
ALSFRS-R, ALS Functional Rating Scale Revised version; behav, saccadic behaviour; CNS-LS, Centre for Neurologic Studies Lability
Scale, Cognistat R/J, Verbal Reasoning and Judgment questions from Neurobehavioural Cognitive Status Examination; COWA,
Controlled Oral Word Association test; def, definite diagnosis; Edu., education; El Escor, El Escorial Criteria; FBI, Frontal
Behavioural Inventory; fMRI, functional magnetic resonance imaging; FVC %pred, forced vital capacity percent predicted when
sitting; HADS D/A, Depression and Anxiety measures of Hospital Anxiety and Depression Scale; Hand, Modified Edinburgh
Handedness Inventory; kg, kilograms; LLT, lower limb total of Manual Muscle test; Med, Medications; Mo. since diagn., months
since diagnosis; MoCA, Montreal Cognitive Assessment; MVIC, maximum voluntary isometric contraction; n/a, not applicable;
prob, probable diagnosis; Struct, structural anatomical scan; ULT, upper limb total of Manual Muscle test; VFI verbal fluency
index; Yrs, years; '(blank)' not assessed; '?' no information
ALS
(n=21)
Medications:
Analgesic
1
Antianxiety
3
Antidepressant
5
Antiepileptic
1
Antihypertensive
7
Atypical antipsychotic
1
Baclofen
6
Riluzole
6
Other†
9
Exclusions:
resp, respiratory compromise
tr, failed eye tracking
ax, HADS anxiety score considered abnormal
f, could not perform task
nc, neurological confound (stroke causing significant atrophy)
mo, motion > 3 mm in any given direction
mt, magnet trigger failed
fs, failed safety screening
clau, claustrophobia
†Antibiotic, Antihistamine, Antiarthritic,
Anticholesterol, Gastric acid treatment,
Anticoagulant, Aspirin, Antidiabetic,
Antiinflammatory, COPD treatments
33
Only 12 ALS patients (ages 44 – 76, 2 females, mean age = 61.6, SD ± 9.6) completed all
experimental components and were used in the final functional analysis. Behavioural data were
successfully acquired from 13 ALS patients, while structural images were available from 16
patients. Twelve controls were age-matched within 3 years of ALS patients (ages 41 – 76 years, 3
females, mean age = 62.6, SD = 10.8) and were included for comparison in the functional
analysis. Thirteen age-matched controls were used for comparison in the behavioural analysis,
while 16 controls were used for structural analysis.
No subjects reported a history of diagnosis with other neurological or psychiatric conditions,
with the exception of one ALS patient who experienced menopausal-related anxiety over 20
years previous. No subjects had any visual disturbances and all subjects had normal or
corrected-to-normal vision, and two ALS patients verified that they could distinguish the stimuli
without their glasses. All participants included in the final analyses (behaviour, function, and
structure) had no history of stroke or traumatic brain injury, with the exception of two ALS
patients who each experienced one concussion over 30 years ago. Because of the difficulty
associated with collecting fMRI data from ALS patients due to the physical restrictions of the
disease (such as compromised breathing), we felt the benefits of including the patients with
histories of concussion and menopausal anxiety to have a larger data set would outweigh the
risks associated with including them, and therefore these patients were not excluded.
Subjects participated in a two-session experiment at the Kingston General Hospital and at
the Queen’s University Magnetic Resonance Imaging facility
(http://www.queensu.ca/neuroscience/MRI-facility.html), respectively, with each session lasting
1-2 hours in length. In the first session, controls were screened for mild cognitive impairment
34
using the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005) or the Mini Mental
Status Examination (MMSE) (Folstein, Folstein, & McHugh, 1975) and were excluded if they
scored below a cognitively normal range (<26/30). ALS patients underwent a more rigorous
battery of neuropsychological evaluations, in addition to having a clinical evaluation in the first
session to evaluate the severity of their functional decline (see Section 2.2.2). Both controls and
patients completed the second session on a separate day, which involved partaking in the MRI
study and performing a saccadic eye movement paradigm (see Section 2.2.3). Control subjects
always completed the first and second sessions within three months of each other, however due
to the rapid functional decline associated with ALS, patients completed the first and second
sessions within 5-30 days to avoid substantial degeneration between the clinical evaluation and
the behavioural and imaging evaluations.
2.2.2 Clinical Evaluation
Clinical evaluation of ALS patients was modelled after a previously described rapid screening
battery used in ALS patients to measure physical function and frontal lobe impairments
(Flaherty-Craig et al., 2006). These measures included pulmonary function tests of forced vital
capacity in terms of percent predicted (FVC %pred) when sitting upright. Patients who had a FVC
< 60%pred were considered to have insufficient pulmonary function to lie supine in the MRI, and
therefore were excluded from completing the second session (N=2) (Table 1). The selfadministered ALS Functional Rating Scale Revised version (ALSFRS-R) was included to evaluate
the degree of physical impairment in ALS (Cedarbaum et al., 1999).
Neuropsychological evaluation using the rapid screening battery included the
Neurobehavioural Cognitive Status Examination (COGNISTAT) (Kiernan, Mueller J., & Langston J.
35
W., 1995) to assess verbal reasoning and judgment. The Controlled Oral Word Association test
(COWA) (Benton, 1969) was used to assess verbal fluency (VF), which is highly sensitive to
executive dysfunction in ALS (Abrahams et al., 2000; Frank et al., 1997; Kew et al., 1993a; Strong
et al., 1999). In the COWA, patients were asked to generate as many words as possible starting
with the letters C, A, and S (excluding proper nouns and repeated words) within an allotted 3
minute time period. These letters were modified from the original COWA which included F, A,
and S in order to avoid repetition from the MoCA (Nasreddine et al., 2005) where subjects were
also asked to generate words starting with the letter F. The letter C was chosen as a
replacement because it represented a consonant of equal difficulty level in word generation
tasks (Borkowski, Benton, & Spreen, 1967). VF represents the average time taken to think of
each word, as calculated in equation (1) (Abrahams et al., 2000), and is designed to control for
individual variations in motor speed. Previously described normative data were used as a
benchmark for age-matched control performance (Tombaugh, Kozak, & Rees, 1999).
Equation 1: Verbal fluency calculation (Abrahams et al., 2000).
Additional clinical evaluations included manual muscle testing based on the Medical
Research Council (1943) scale, manual strength tests of maximum voluntary isometric
contraction (MVIC) of the dominant hand, a detailed patient history, age and symptoms at
disease onset, disease duration and current medications being taken by the patient. Further
neuropsychological testing included the highly sensitive MoCA to detect mild cognitive
36
impairments in executive functions, working memory, attention and concentration, abstract
reasoning, visuospatial abilities, language and orientation (Nasreddine et al., 2005). The Frontal
Behavioural Inventory (FBI) (Kertesz, Davidson, & Fox, 1997), which is widely approved in FTD
literature and has been employed in ALS research (Heidler‐Gary & Hillis, 2007), was completed
by a family member or caregiver to rate changes in behaviour and/or personality of the patient,
where a score of 27 or more would be sufficient for FTD diagnosis.
Supplementary tests were administered to measure mood dysfunction and pseudobulbar
affect using the Centre for Neurologic Study Lability Scale (CNS-LS) (Moore, Gresham, Bromberg,
Kasarkis, & Smith, 1997) and the Hospital Anxiety and Depression Scale (HADS) (Bjelland, Dahl,
Haug, & Neckelmann, 2002; Zigmond & Snaith, 1983). The HADS was modified to exclude one
question which falsely exaggerated the measure of depression due to the physical disabilities
experienced by ALS patients, as previously modified for an ALS population (Abrahams et al.,
2000). One subject (subject 16, Table 1) would have been excluded due to an abnormally high
anxiety score on the HADS; however this patient never completed the MRI session due to
respiratory compromise. Of the 12 patients included in the final analyses, most were righthanded with the exception of four who were functionally left-handed, as determined by the
Modified Edinburgh Handedness Inventory (Oldfield, 1971).
2.2.3 Saccade paradigm
Subjects lay supine in the scanner and visual stimuli were back-projected onto a display
screen placed at the head of the scanner. A mirror angled at approximately 45° was attached to
the head coil to allow subjects to see the display screen. A text display instructed the subjects to
prepare for the onset of the experimental run which consisted of 64 trials in total. Each
37
experimental run consisted of 16 ‘prosaccade’ trials, 16 ‘antisaccade’ trials, 8 ‘pro-catch’ trials, 8
‘anti-catch’ trials, and 16 fixation trials and lasted 277.5 s in total. In this paradigm, each saccade
and catch trial lasted 4500 ms; equivalent to the length of 3 repetition times (TRs), each 1.5 s
long (see Section 2.2.7). Each subject successfully completed anywhere from 3-7 runs, as time
and subject comfort permitted.
Each experimental run began with a 3 s (2 TRs) fixation period to allow the longitudinal
magnetization of the fMRI signal to reach a steady state before commencing data acquisition.
Saccade trials within a run started with a 1000 ms display of a central fixation point (a hollow
‘gold coin’, visual angle approximately 1°) that did not convey any instruction other than to
fixate at centre (Fig. 2.1). The fixation point was then replaced by one of two instructional
stimuli presented for 1300 ms. Instructional stimuli were either a green turtle instructing a
prosaccade (to look towards the target) or a red crab instructing an antisaccade (to look away
from the target). These stimuli were chosen to match a paradigm designed for acrosspopulation comparisons to the other clinical groups studied in our lab that includes young
children. Next, all visual stimuli disappeared to reveal a black screen for a 200 ms ‘gap’ period
before a peripheral target (hollow ‘gold coin’, at 4.5 or 8° of visual angle to allow participants to
move only their eyes and not their head) pseudorandomly appeared to the right or left of the
central fixation for 100 ms to prevent prediction of target location. The gap period was used to
elicit the most automatic response possible by allowing for disengagement of active visual
fixation prior to target appearance (Dorris & Munoz, 1995; Saslow, 1967). After the
disappearance of the peripheral target, a black screen reappeared for 1400 ms and subjects
were instructed to hold their gaze at the target location for a prosaccade trial, or in the opposite
38
location of the target for an antisaccade trial. Subjects were asked to: 1) return their gaze to the
central fixation point, which reappeared at the centre of the screen, and 2) to maintain fixation
for 500 ms until the trial ended. Before beginning the experiment, subjects were instructed to
correct themselves if they made an incorrect saccade on a trial (a direction error, such as an
Figure
3
erroneous prosaccade on an antisaccade trial), in order to convey that they understood the task
but made an error of which they were aware.
Prosaccade
Trial
N=16
Antisaccade
Trial
N=16
Pro-catch
Trial
N=8
Anti-catch
Trial
N=8
1000ms
1300ms
200ms
100ms
1400ms
500ms
Duration
Figure 2.1: Experimental paradigm. An illustration of the stimuli and duration of events for the
four trial types. Figure adapted with permission from Cameron et al. (2011) in press.
On catch trials, subjects were instructed to continue fixating at centre for a period of 1700
ms of darkness until the central fixation point reappeared to end the trial. Trials containing only
fixation varied in length such that out of the 16 fixation trials, eight of them were 1 TR, four of
were 2 TRs, and four were 3 TRs in length. All runs ended with a fixation period of 16.5 s to allow
39
for the hemodynamic response to return back to the baseline signal before the commencement
of the next run.
2.2.4 Eye tracking and visual display
Eye position was recorded using an ISCAN ETL-400 camera (Burlington, MA, USA) at a
sampling frequency of 120 Hz. The camera was positioned approximately 50 cm from the bore
hole at the head end of the magnet, next to a custom built screen where visual images were
back-projected using a NEC LT265 DLP video projector (Tokyo, Japan) with a refresh rate of 60
Hz and a resolution of 1024 X 768. An infrared fibre-optic illuminator was attached to the head
coil and angled at approximately 45° to illuminate the subject’s right eye. Visual images were
created using E-PRIME software (Psychology Software Tools Inc., Pittsburgh, PA, USA) on a PC.
Eye position was calibrated before the first functional scan using a nine point calibration
presentation. Display software was triggered with a TTL pulse from the MRI to ensure the eye
position recordings and MRI scans were synchronized.
Three ALS patients were excluded from analysis due to eye tracking difficulties (Table 1).
Due to ineffective calibration, the pupil trace was often lost and substantial portions of eye
position data were therefore unavailable. One patient was excluded from functional analysis
because the TTL pulse did not fire correctly during fMRI acquisition; therefore only the eye
tracking data were used for analysis. Most patients who had successful eye tracking completed
five to seven runs, although two patients only had time to complete three or four runs due to
extra time spent calibrating and adjusting the ISCAN camera.
40
2.2.5 Functional magnetic resonance imaging parameters
All scans were obtained using a Siemens 3 Tesla Magnetom Trio system (Erlangen, Germany)
with a receiving-only 12-channel head coil. An initial localizer scan was taken to identify where
the head was located in the magnet in order to position the slices for all subsequent scans. Highresolution anatomical scans were obtained using T1-weighting with an MP-RAGE 3D sequence
(repetition time, TR = 1760 ms; echo time, TE = 2.2 ms; flip angle, FA = 9°; field-of-view, FOV =
256 x 256 mm; matrix size 256 x 256; 1 mm iso-voxel resolution; 176 volumes). Experimental
runs were presented in a rapid event-related fMRI design, and images were obtained with T2*weighted echo-planar imaging (EPI) sensitive to changes in BOLD signal contrasts (Kwong et al.,
1992; Ogawa, Lee, Kay, & Tank, 1990). Slices were taken in the transverse plane and were
angled slightly to avoid the eyes, with a phase-encoding direction of anterior-posterior. Twentyfour slices (3.3 mm thick) covered the frontal, parietal and occipital areas of the brain, and
extended down to the ventral striatum to include the basal ganglia. Functional volumes were
collected in an interleaved manner (TR =1500 ms; TE = 30 ms; FA = 72°; FOV = 211 x 211 mm;
matrix size 64 x 64; 3.3 mm iso-voxel resolution; 185 volumes).
2.2.6 Behavioural analysis
Analysis of behavioural data was performed using custom-made scripts in Matlab v7.90 (The
MathWorks Inc., Natick, MA, USA). No differences were seen across rightward and leftward
saccade trials; therefore all trials were pooled together. Correct trials were separated from
incorrect trials which consisted of: direction errors (even if they were subsequently corrected),
failure to respond, anticipatory errors (saccadic reaction time; SRT < 90 ms), delayed saccades
(SRT > 1000 ms), multiple saccades following a correct response, and saccades during catch or
41
fixation trials. No subjects (ALS or control) exceeded plus or minus three standard deviations
(SD) from their group mean on measures of SRT, coefficient of variation of SRT (CVSRT), express
saccades, or direction errors for either prosaccade or antisaccade tasks.
Using SPSS Statistics v19.0 (IBM, Chicago, IL, USA), 2 x 2 repeated measures ANOVA’s were
performed on measures of saccade performance (mean SRT, mean CVSRT, and percentage of
direction errors) and saccade metrics (mean amplitude, mean velocity). The variables were
Group with two levels (ALS, controls) and Task with two levels (pro, anti). Main effects of task
were expected to be seen across the above measures of saccade performance (such as SRT and
errors), due to the extensive documentation of such differences between pro- and antisaccades
(Donaghy et al., 2010a; Munoz et al., 1998; Munoz & Everling, 2004; Shaunak et al., 1995).
Significant differences across Groups (ALS and controls) will be reported in the Results section
but will not be explicitly highlighted in the Figures for the sake of figure clarity. Significant
differences across Task and Group-Task interactions will be explicitly highlighted in the Results
section and Figures.
Express saccades were defined in this study as saccades with latencies between 90 – 160 ms
of target onset. An independent t-test was used for analysis of express saccades on prosaccade
trials only, since generation of an anti-express saccade is physiologically impossible due to the
involvement of multiple cortical areas in generating antisaccades (Everling et al., 1998; Everling
et al., 1999; Everling & Munoz, 2000). Two-tailed Pearson correlations were performed between
neuropsychological test scores and the proportion of antisaccade errors to determine whether
any clinical variables were related to oculomotor performance.
42
2.2.7 Functional analysis
Imaging data were analyzed using Brain Voyager QX v1.10 (Brain Innovation, Maastricht,
The Netherlands). The first two functional volumes acquired were discarded from analysis
(‘dummy scans’) to ensure the longitudinal magnetization had reached steady-state. Of the
remaining volumes, functional imaging data were pre-processed by applying: 3D motion
correction with trilinear interpolation to align images to the first volume within each run, slice
scan-time correction with a cubic-spline interpolation, high-pass temporal filtering (cut off of 2
cycles/run and linear trend removed), and spatial smoothing using a 4 mm FWHM Gaussian
kernel. Coregistration was performed to align each functional scan to the structural image.
Normalization was performed by aligning anatomical images with the anterior commissure posterior commissure (AC-PC) plane to convert images into Talairach space (Talairach &
Tournoux, 1988) using trilinear interpolation.
In the rapid event-related fMRI design, the rapid succession of trials causes the time-locked
hemodynamic response of each event to overlap significantly. To correct for this, a
deconvolution-based general linear model (GLM) was used in Brain Voyager to predict the
hemodynamic response associated with each trial type of interest (Dale & Buckner, 1997). A
number of ‘stick predictors’ were created based on trial types of interest and included: ‘correct
prosaccades’, ‘correct antisaccades’, ‘correct prosaccade preparation’, ‘correct antisaccade
preparation’ trials, erroneous pro- and anti-‘direction errors’ that were corrected and ‘null trials’
of no interest. Stick predictors were modeled over a 13 point time scale to cover the temporal
length of a typical hemodynamic response of 20 s with a resulting temporal resolution of around
1 TR (20 s/13 ≈ 1.5 s). Correct fixation trials were not modelled since fixation was the control
43
state (or baseline) in this experiment, where trial types of interest were subtracted from fixation
to produce the imaging contrasts (Ollinger et al., 2001). Remaining incorrect trials (including
uncorrected errors, multiple eye movements per trial, correct trials that were subsequently
‘uncorrected’) and trials where tracking was lost were binned with the ‘null trials’ predictor to
prevent them from interfering with the predicted BOLD response for the modeled trial types
(Brown et al., 2007).
Main contrast analysis
ALS and control groups were analyzed separately using random-effects GLMs with separate
subject predictors and Z-normalization to highlight which oculomotor areas were recruited for
prosaccades and antisaccades. The 5th to 7th time points from the onset of a saccade trial (7.7,
9.3, 10.8 s) were used for the main contrast as these time points corresponded to the peak
hemodynamic response upon examination of the signal a priori. Statistical maps were generated
for each group with a threshold set to p < 0.001 (t value = 4.44, df = 11; uncorrected) and were
cluster-corrected across the population of voxels with p <0.05 (10 contiguous voxels, as
estimated by Brain Voyager’s Cluster-level Statistical Threshold Estimator with 1000 iterations).
Group-level statistical maps were overlaid onto averaged structural images containing the
anatomical scans (in Talairach space) of all twelve subjects in each group. These statistical maps
were used in subsequent region of interest (ROI) analyses involving preparation and execution.
ROI analysis
Random-effects GLMs were used to extract beta weights (GLM parameter estimates) from
five ROIs, where contrasts of interest were prosaccade preparation and execution, and
antisaccade preparation and execution. ROIs were selected as 125 contiguous voxels (5 x 5 x 5),
44
in the form of a cubic cluster, surrounding the peak activation within DLPFC, FEF, SEF, PEF, and
CD. These oculomotor areas were established based on anatomical landmarks and known
locations within Talairach space. To isolate pro- and antisaccade preparation (pro and anti prep),
BOLD activations were taken from the 5th and 6th time points of the pro-catch and anti-catch
trials minus fixation trials. To isolate pro- and antisaccade execution (pro and anti exec), BOLD
activations were taken from the 6th and 7th time points of prosaccade and antisaccade trials
minus pro-catch and anti-catch trials. Beta weight estimates were then extracted from these
BOLD contrasts of interest using the 5th and 6th time points for preparation, meanwhile the 6th
and 7th time points were used for execution because the onset of the peripheral target was
delayed by 1TR (1.5 s) from the instructional cue (Brown et al., 2007). Beta weights from each
ROI were averaged across the right and left hemispheres to increase statistical power for all
contrasts. Two-way repeated measures ANOVA’s were conducted on the ROI beta weight values
for preparation and execution contrasts, where the variables were: 1) Task with two levels (pro,
anti), and 2) Group with two levels (ALS, control). Pearson correlations (two-tailed) were
performed to determine if any of the ROI mean peak beta weights predicted various measures
of saccade performance, or whether they predicted neuropsychological scores as described in
the Results and Figure captions.
Main effects of task were expected to be seen across the selected ROIs when comparing
BOLD activation due to the extensive documentation of such differences between pro- and
antisaccades in neuroimaging literature (Brown et al., 2007; Connolly et al., 2002; Connolly et
al., 2005; Curtis & D'Esposito, 2003a; Curtis & D'Esposito, 2003b; Curtis & Connolly, 2008;
DeSouza et al., 2003; Ford, Goltz, Brown, & Everling, 2005). For this reason, statistical
45
differences in the fMRI data across tasks will be reported in the Results section but will not be
explicitly highlighted in the Figures for the sake of figure clarity. Meanwhile, significant
differences across Groups (ALS and control) will be explicitly highlighted in the Results and
Figures.
2.2.8 Structural analysis
Structural analysis was performed using CIVET pipeline software developed by Alan Evans
and colleagues at the Montreal Neurological Institute (Ad-Dab'bagh, 2005; Ad-Dab'bagh et al.,
2006; Boucher, Whitesides, & Evans, 2009; Chung et al., 2003; Chung & Taylor, 2004; D. L.
Collins, Neelin, Peters, & Evans, 1994; Grabner et al., 2006; Kim et al., 2005; Lerch & Evans,
2005; Lyttelton, Boucher, Robbins, & Evans, 2007; MacDonald, Kabani, Avis, & Evans, 2000;
Robbins, 2004; Sled, Zijdenbos, & Evans, 1998; Smith, 2002; Tohka et al., 2004; Tohka,
Zijdenbos, & Evans, 2004; Zijdenbos, Forghani, & Evans, 1998) using T1-weighted MRI images.
Outputs of this pipeline included transformations from native to linear to MNI space, partial
volume estimates, segmented volumes, surface maps and cortical thickness maps. Cortical
thickness was investigated for significant differences between ALS and control groups across the
whole brain at the 95% significance level and was corrected for multiple comparisons using false
discovery rate. Analyses were performed using a GLM accounting for age, gender, and group.
Segmentations of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) volumes
were used to investigate significant differences between ALS and controls using independent ttests. Additionally, partial volume estimations of GM in the four lobes and cerebellum, in
addition to more specified areas such as medial frontal lobes, were tested for significant
differences between ALS and control subjects using independent t-tests.
46
2.3 Results
2.3.1 Clinical and Neuropsychological Evaluation
In total, all of the 21 patients recruited for this study completed the clinical evaluation and
19 completed the neuropsychological evaluation. Scores from these evaluations are reported in
Table 1. All patients met the El Escorial criteria for ‘definite’ diagnosis, with the exception of four
patients who met the criteria of ‘probable’ diagnosis. Mean disease duration at the time of the
clinical evaluation was 32.6 months (range, 10-168), and the mean vital capacity was 80%
predicted (FVC range, 38-101). The evaluation of physical disability using the ALSFRS-R revealed
a mean score of 34.4 ± 6.2 (SD) out of 48, where a score of 48 is considered completely
independent and functional. The mean score from this ALS group was functionally worse than a
previously reported average score of 38 from a sample demographic of 384 ALS patients with a
mean disease duration of 25 months (Cedarbaum et al., 1999).
Patients and controls were evaluated for mild cognitive impairment using either the MMSE
or MoCA. Specific scores for the controls were not tabulated, but a score below 26 would merit
exclusion of a control subject; therefore we can assume that, on average, controls scored
greater than or equal to 26. The mean MoCA score of ALS patients was 23.2 ± 4.3; therefore it is
reasonable to assume that, on average, the ALS group displayed mild cognitive impairment that
fell below normal standards of performance (26 or higher) (Nasreddine et al., 2005).
In depth neuropsychological evaluation revealed mean COGNISTAT reasoning scores of 6.1 ±
1.8 and judgment scores of 5.1 ± .9, both which fell within the average ranges reported for
healthy adults (Kiernan et al., 1995). Verbal fluency calculations showed a mean score of 8.3 ±
47
5.2 (range 2.9 - 19.3) which is more impaired than previously noted in ALS populations without
pseudobulbar palsy (Abrahams et al., 1997; Abrahams et al., 2000; Abrahams et al., 2005; Frank
et al., 1997; Massman et al., 1996). Overall, ALS patients did not display frontal lobe dementia,
as the average FBI score for ALS participants was 11.0 ± 10.0 and a score of 27 or higher is
required for a diagnosis of frontal lobe dementia (Kertesz et al., 1997). The FBI score was
extremely variable due to a handful of patients who came close to the cut off for frontal lobe
dementia and one patient who met the criteria with a score of 42. Emotional lability, or
dramatic mood fluctuations, was measured using the CNS-LS, where a score of 13 has been
demonstrated as a reliable cut off for accurately predicting a neurologist’s diagnosis of
emotional lability in 82% of ALS participants (Moore et al., 1997). Based on this cut off, the ALS
group can be considered borderline labile with a mean score of 12.4 ± 4.1. By these standards,
out of the 19 ALS participants who completed the CNS-LS, 9 of them were considered
emotionally labile. The HADS evaluation revealed that as a group, the ALS participants did not
display signs of depression or anxiety, except for one patient who fell within the abnormal range
for anxiety (patient 16, Table 1); however this patient also met other exclusion criteria and
therefore did not complete the experimental paradigm due to a low FVC causing respiratory
compromise. Of the remaining patients, the average depression score of 2.85 ± 2.80 and anxiety
score of 5.16 ± 2.93 fell within the normal range of 0-7 points (Zigmond & Snaith, 1983). Based
on the large variability on the mean scores for each neuropsychological test, we can conclude
that the ALS subjects in this study all had varying degrees of impairment perhaps because
patients were at different stages in their disease progression, and they do not represent a
homogenous sampling. However, in general, the ALS patients in this study demonstrated mild
48
cognitive impairment and executive dysfunction based on their performance on
neuropsychological evaluations, despite the fact that most patients did not meet the criteria for
frontal lobe dementia.
2.3.2 Behaviour
Saccadic eye movement tasks, such as the antisaccade, can be employed to investigate
executive function. In a disease where executive dysfunction is present, ALS patients were
hypothesized to display difficulties with inhibiting automatic saccades. Illustrated in Fig. 2.2 is
the cumulative distribution of SRTs for the prosaccade and antisaccade tasks, displayed as a
proportion of the total number of trials, where the latencies of correct and incorrect saccades
were categorized into SRT bins of 10 ms increments. The greatest proportion of correct, shortlatency saccades were executed by ALS patients on prosaccade trials, while direction errors
were virtually negligible. The latencies of correct prosaccades made by control subjects were
considerably more delayed than those of the ALS patients, and slightly more direction errors
were made by the controls on prosaccade trials.
We analyzed cumulative SRT distributions using the non-parametric Kolmogorov-Smirnov
test to determine whether the distributions of SRTs were significantly different across groups for
correct and error trials (i.e., how much the distributions differed from each other). We found
significant differences in SRT distributions between ALS and control subjects for correct
antisaccades (K= 1.435, p< .033), incorrect antisaccade trials (K= 4.489, p< .001), and incorrect
prosaccade trials (K= 2.490, p< .001). Distributions of correct prosaccade SRTs between ALS and
controls were not significantly different when all SRT bins were included (K= 1.272, p< .079),
49
however SRT distributions in the express saccade epoch only (90 – 160 ms) were significantly
different between groups (K= 1.500, p< .022).
1
†
†
0.6
0.4
0.2
Correct
Proportion of Saccades
0.8
ALS Pro
CTRL Pro
ALS Anti
CTRL Anti
= p< .05
=
‡ p< .001
Express
saccade
epoch
90 – 160 ms
†
0
-0.2
150
200
250
300
350
400
450
500
550
600
‡
‡
Error
100
SRT Bin (ms)
-0.4
Figure 2.2: Cumulative distribution of saccadic reaction times (SRTs). Correct saccades are
illustrated as positive proportions, while direction errors are shown as negative proportions.
Combined, the proportion of correct and error trials add up to the total number of trials (total
proportion of 1).
Control subjects demonstrated faster latencies on correct antisaccade trials than the ALS
subjects, who lagged closely behind. The most direction errors were made during antisaccade
trials by ALS subjects. These antisaccade errors occurred mostly between 100 and 180 ms after
the target appearance, and were likely in response to inadequate suppression of the automatic
prosaccade, whereas antisaccade errors made by the controls were fewer and more delayed.
50
Shown in Fig. 2.3 are the behavioural results of prosaccade and antisaccade trials in ALS and
control subjects. Analysis of variance for SRT (Fig 2.3A) showed a significant main effect of Task
(F(1,12)= 41.961, p< .001), with significantly prolonged antisaccade latencies, which has been
previously shown in the ALS literature (Donaghy et al., 2010a; Donaghy et al., 2010b; Shaunak et
al., 1995). Contrary to our predictions, there was no main effect of Group (F(1,12)= .271, p=
.612), while Group by Task interactions failed to reach significance (F(1,12)= 4.145, p= .064).
Intrasubject variability of SRT was expressed as a coefficient of variation (CVSRT). A main
effect of Task was found for CVSRT (F(1,12)= 22.832, p< .001) (Fig. 2.3B) such that prosaccades
were significantly more variable than antisaccades. A noticeable trend of a Group main effect
was seen (F(1,12)= 3.752, p= .077), where ALS patients had more variability for CVSRT than
controls when prosaccade and antisaccade trials were pooled together. Increased CVSRT is
common in many patient groups (Cameron et al., 2011; Munoz et al., 2003; Peltsch, 2011),
generally as a result of the variability of the disease across patients. The interaction between
Group and Task reached significance (F(1,12)= 7.083, p= .021), and further analysis showed
prosaccade CVSRT was significantly greater than that of antisaccades within both ALS (p= 0.003)
and control groups (p= 0.035). Further analysis of the Group-Task interactions also revealed ALS
patients displayed significantly greater prosaccade CVSRT than controls (p= .033).
51
Control
300
†a p<0.05
‡ p<0.01
**b p<0.01
100
0
Pro
50
‡
40
30
20
10
0
Pro
†
Pro
D
40
30
‡
†
Anti
Direction Errors (%)
Express Saccades (%)
C
60
50
40
30
20
10
0
†
†
20
10
0
Pro
Anti
E
**
MoCA Score
SRT(ms)
ALS
400
200
**
B
N=13
**
500
CV SRT
A
Anti
a = significance for Group-Task interactions only
b = significance for main effects of Task only (no main effects of Group shown)
30
25
20
15
10
5
0
r = -0.69
0
20
40
60
‡
80
100
Antisaccade Errors (%)
Figure 2.3: Behaviour. (A) Mean saccadic reaction time (SRT) on correct trials. (B) Mean intrasubject coefficient of variation (CV) for SRT. (C) Mean percentage of express saccades (90 – 160
ms). (D) Mean percentage of direction errors. (E) Correlation of Montreal Cognitive Assessment
(MoCA) score (/30) and percentage of antisaccade direction errors. Error bars represent
standard error of the mean (SE).
The express saccade epoch was defined as 90 –160 ms (Fig. 2.2). An independent t-test
revealed that the ALS group made a significantly greater proportion of express saccades on
prosaccade trials compared to controls (t(24)= 3.364, p= .003) (Fig. 2.3C). Analysis of variance of
direction errors revealed no main effect of Group (F(1,12)= 1.232, p= .289) (Fig. 2.3D). However,
there was a significant main effect of Task (F(1,12)= 11.100, p= .006) and significant interaction
between Group and Task (F(1,12)= 4.749, p= .050). Follow-up analysis showed the proportion of
direction errors was significantly greater on antisaccades than prosaccades for both the ALS (p=
.012) and control groups (p= .034). There was only a trend between groups for direction errors,
where ALS patients seemed to make more antisaccade direction errors (p= .125) while controls
52
seemed to make more prosaccade direction errors (p= .109). A two-tailed Pearson’s correlation
revealed a significant negative correlation between the ALS group’s performance on the MoCA
and the percentage of antisaccade errors made (r(12)= -.686, p< .010) (Fig. 2.3E), where
antisaccade error rates increased as MoCA scores worsened.
Figures 2.4A and B illustrate the metrics of pro- and antisaccades in terms of saccade
amplitude and velocity, respectively, across ALS and control subjects. Analysis of variance
revealed no significant main effect of Group on saccade amplitude (F(1,12)= .504, p= .491) or
saccade velocity (F(1,12)= .749, p= .404), suggesting ALS patients did not have significantly
altered saccade metrics. A main effect of Task was observed for saccade amplitude (F(1,12)=
6.012, p= .030), where antisaccades had significantly greater amplitudes than prosaccades
despite the target locations remaining constant at either 4.5 or 8° of visual angle. These results
differed from previous findings in elderly controls, where accuracy of amplitude worsened with
age on the prosaccade task, but antisaccade accuracy was preserved (Guitton et al., 1985;
Olincy, Ross, Youngd, & Freedman, 1997). A significant interaction effect between Group and
Task on saccade amplitude (F(1,12)= 15.296, p= 0.002) was followed by pairwise comparisons
which revealed antisaccades had significantly greater amplitudes than prosaccades within the
ALS group only (p=0.003). In general, both ALS and control subjects appeared to generate
slightly hypometric saccades, where the subjects undershot the target location. Saccade velocity
showed no main effect of Task (F(1,12)= 2.708, p= .126), and displayed a strong trend of an
interaction effect between Group and Task, although this failed to reach significance (F(1,12)=
4.424, p= .057).
53
B
A
Amplitude (°)
8
N=13
‡
ALS
6
Control
4
‡a p<0.01
*b p<0.05
2
300
Velocity (°/sec)
*
10
200
100
0
0
Pro
Anti
Pro
Anti
a
= significance for Group-Task interactions only
b = significance for main effects of Task only (no main effects of Group shown)
Figure 2.4: Saccade metrics. (A) Mean saccade amplitude. (B) Mean saccade velocity. Error bars
represent standard error of the mean (SE).
Our focus was to evaluate group differences in saccadic behaviour across ALS and control
subjects. In summary, ALS patients seemed to be biased towards automatic responses, as
demonstrated by a greater proportion of express saccades, more variable prosaccade SRTs, and
a trend of more errors on antisaccade trials than the controls. It is possible that this automaticity
stems from an inability to produce the appropriate task set, and is unlikely a problem linked to
saccade execution since saccade metrics were consistent across ALS and control groups. We
therefore isolated saccade preparation (or task set) from execution in our fMRI analysis to
determine the neurological process which might be influencing this behaviour.
2.3.3 fMRI
fMRI analysis was conducted to compare the activation in key oculomotor areas of the brain
during prosaccade and antisaccade tasks (correct saccades only) across the ALS and control
groups to determine how brain function might be influencing saccade behaviour. The goal was
to investigate whether differences in brain activation during saccade preparation and execution
would explain the ALS patients’ bias towards automaticity and their subsequent difficulty with
54
performing the antisaccade task correctly. More specifically, we predicted differences across
ALS and control subjects in the amount of BOLD signal in brain areas involved with executive
functions and inhibiting unwanted saccades such as DLPFC and FEF (Pierrot-Deseilligny et al.,
1991a; Pierrot-Deseilligny et al., 2003; Rivaud et al., 1994) during pro- and antisaccade
preparation. We also expected changes in BOLD signal in other oculomotor regions that
modulate SC activity such as SEF, PEF, and CD.
Main contrast analysis
A contrast showing correct prosaccade trials plus correct antisaccade trials was first created
(Fig. 2.5) to confirm that both ALS and control participants were able to recruit (or “bring
online”) key oculomotor regions of interest (ROIs) known to be involved with producing
prosaccades and antisaccades including the DLPFC, FEF, SEF, PEF, and CD (Brown et al., 2007;
Connolly et al., 2005; Connolly et al., 2007; Curtis & D'Esposito, 2003b; DeSouza et al., 2003;
Everling & Munoz, 2000; Gottlieb, Kusunoki, & Goldberg, 1998; Guitton et al., 1985; Kusunoki,
Gottlieb, & Goldberg, 2000; Pierrot-Deseilligny et al., 1991a; Schlag-Rey & Amador, 1997;
Watanabe & Munoz, 2009). This contrast was made using the 5th, 6th and 7th time points (see
Methods) to show brain activation during the peak hemodynamic response. Significant BOLD
activations were seen in all ROIs in both the ALS and control averaged statistical maps.
55
Anti + Pro
Antisaccade + Prosaccade
Control
ALS
N=12
Z=50
t-value 8.00
SEF
FEF
PEF
Y=26
4.44
DLPFC
-4.44
Z=10
CD
-8.00
p < 0.001
R
L
Figure 2.5: Anti- and prosaccade contrast map. Contrast map of prosaccade trials and
antisaccade trials added together, cluster size corrected at p < .05 (10 contiguous voxels).
Significant BOLD activations were observed in all ROIs (‘hot’ colours) and are labelled. Talairach
coordinate values of planes are given.
Talairach coordinates for the peak activations that exceeded the statistical threshold in each
ROI are shown in Table 2. Having established that both ALS and control subjects were able to
recruit these key oculomotor areas to perform the prosaccade and antisaccade tasks, we next
focused on ROI analysis of BOLD activations during saccade preparation and saccade execution.
56
Group and Region
of Interest
ALS
right DLPFC
left DLPFC
right FEF
left FEF
SEF
right PEF
left PEF
right CD
left CD
Control
right DLPFC
left DLPFC
right FEF
left FEF
SEF
right PEF
left PEF
right CD
left CD
Prosaccade plus Antisaccade
X
Y
Z
t
Vol
39
-39
18
-42
6
30
-42
18
-18
35
29
-7
-4
-1
-46
-46
-1
8
28
34
67
40
58
37
43
19
10
11.39
7.81
10.65
11.09
12.75
12.73
12.46
10.54
8.34
76
67
146
174
459
445
548
147
14
36
26
28
10.12
100
24
-18
6
45
-21
15
-15
-4
-13
-10
-40
-64
5
6
55
58
58
40
49
10
7
16.65
12.17
9.17
12.86
12.60
6.84
6.72
507
289
177
1000
391
Table 2: Talairach coordinates (X, Y, Z) of peak activation in GLM contrast maps for the
antisaccade + prosaccade contrast from Fig. 2.5. DLPFC, dorsolateral prefrontal cortex; FEF, SEF,
PEF, frontal, supplementary, parietal eye fields; CD, caudate nucleus
Saccade Preparation
To adequately prepare for a pro- or antisaccade, a task-appropriate amount of build-up
neural activity (known as preparatory set) is required in brain regions that are responsible for
producing saccades (Dorris et al., 1997; Dorris & Munoz, 1998; Everling et al., 1998; Everling et
al., 1999; Everling & Munoz, 2000). On correct antisaccade trials, adequate inhibition of the
saccade map is required to prevent erroneous prosaccades (Everling et al., 1998; Everling et al.,
1999; Everling & Munoz, 2000), and has been linked to increased BOLD signal in DLPFC and FEF
57
(DeSouza et al., 2003). Meanwhile, higher levels of pre-target activity in the saccade map are
more desirable for producing fast express saccades (Dorris et al., 1997; Everling et al., 1998;
Everling & Munoz, 2000) – a behaviour that was observed in the ALS group.
Group-level statistical maps showing significant peak BOLD activations during preparation
for pro- and antisaccades were overlaid onto averaged anatomical images and are shown in
Figures 2.6A and B, respectively. Talairach coordinates of the peak voxels from the group-level
maps that survived cluster correction for multiple comparisons (p< .05) and surpassed the
significance threshold (p< .001) for pro- and antisaccade preparation contrasts are listed in Table
3. Beta weights were extracted from each ROI (including those that fell below the threshold of
p< .001) and are shown in Fig. 2.7A. The mean peak beta weights (Fig. 2.7B) for each ROI were
calculated using beta weights from the 5th and 6th time points shown in grey in Fig. 2.7A.
58
Pro-Prep
A
Anti-Catch - Fixation
Control
ALS
Anti-Prep
B
Pro-Catch - Fixation
Control
ALS
Z=50
Z=50
SEF
SEF
FEF
FEF
PEF
PEF
Y=26
Y=26
DLPFC
DLPFC
Z=10
Z=10
CD
R
N=12
CD
L
R
8.00
4.44
p < 0.001 -8.00
-4.44
t-value
N=12
L
8.00
4.44
p < 0.001 -8.00
-4.44
t-value
Figure 2.6: Saccade preparation contrast maps. (A) Contrast map for pro prep created from procatch trials subtracting fixation trials, cluster size corrected at p < .05 (10 contiguous voxels). The
5th and 6th time points relative to trial onset were used in this subtraction. ROIs are labelled with
significant BOLD activations shown as ‘hot’ colours. (B) Contrast map for anti prep created from
anti-catch trials subtracting fixation trials, cluster size corrected at p < .05 (10 contiguous
voxels). The 5th and 6th time points relative to trial onset were used in this subtraction. ROIs are
labelled, with significant BOLD activations shown as ‘hot’ colours.
59
Prosaccade Preparation
Group and Region
of Interest
ALS
right DLPFC
left DLPFC
right FEF
left FEF
SEF
right PEF
left PEF
right CD
X
Y
Z
t
Antisaccade Preparation
Vol
X
Y
Z
t
Vol
30
32
37
8.12
71
27
-30
3
27
-27
-7
-10
2
-55
-67
46
49
55
40
31
9.27
8.32
6.88
7.09
7.08
153
76
47
148
170
36
-27
30
-24
-3
6
-30
12
29
29
-10
-10
-7
-70
-52
2
31 6.86
28 5.97
46 11.35
61 8.84
52 11.13
43 7.41
40 7.86
13 6.39
10
11
86
34
173
129
109
17
left CD
Control
right DLPFC
left DLPFC
right FEF
left FEF
SEF
right PEF
left PEF
right CD
left CD
27
-48
6
6
-24
-10
-4
2
-70
-64
49 11.98
43 10.47
49 9.10
40 7.56
46 8.74
153
29
75
105
128
Table 3: Talairach coordinates (X, Y, Z) of peak activation in GLM contrast maps for prosaccade
and antisaccade preparation contrasts. Coordinates for significant activations only from Fig. 2.6
are shown. DLPFC, dorsolateral prefrontal cortex; FEF, SEF, PEF, frontal, supplementary, parietal
eye fields; CD, caudate nucleus.
60
A
ALS
CTRL
p<0.05
b p<0.05
b p<0.01
†
*
**
BOLD Signal (% Change)
a
SEF
0.6
0.4
0.2
0
-0.2
-0.4
p= .183
0.6
0.4
0.2
0
0.6
0.4
0.2
0
-0.2
-0.4
FEF
0.8
0.6
0.4
0.2
0
-0.2
-0.4
PEF
Mean Peak Beta Weight
ALS Pro-Prep
ALS Anti-Prep
CTRL Pro-Prep
CTRL Anti-Prep
B
DLPFC
0.6
0.4
0.2
0
-0.2
-0.4
0.6
**
0.4
0.2
0
*
0.8
0.6
†
0.4
0.2
0
0.6
**
p= .189
0.4
0.2
0
CD
0.6
0.4
0.2
0
-0.2
-0.4
1.5 4.6 7.7 10.8 13.8 16.9 20
Time (s)
1
3
5
7
9
11
13
0.6
0.4
**
0.2
0
Pro-Prep Anti-Prep
a = significance for Group-Task interactions only
b = significance for main effects of Task only (no
Time Points
main effects of Group to report)
Figure 2.7: Region of interest (ROI) analysis for pro and antisaccade preparation. (A)
Illustration of mean BOLD signal time course for pro and anti prep trials for all ROIs. (B) Mean
peak beta weight values for the 5th and 6th time points from random effects analysis of pro prep
and anti prep trials for 125 cubic voxels surrounding the peak activations in regions displaying
greater activation during catch trials compared to fixation trials. Left and right hemispheres
were averaged. In ROIs where BOLD activation did not surpass the significance threshold (p<
.001), thresholds were lowered until beta weights could be obtained. Errors bars represent
standard error of the mean (SE).
61
Mean peak beta weights for saccade preparation in Fig. 2.7B were tested for statistical
significance using 2 x 2 ANOVA’s. Significant Task effects and Group-Task interactions are shown
in Fig. 2.7B and there were no significant effects of Group to report. SEF showed no main effect
of Group (F(1,12)= .304, p= .593) or Task (F(1,12)= .793, p= .392), but did show a trend
approaching significance for a Group-Task interaction effect (F(1,12)= 3.957, p= .072). FEF
displayed a significant main effect of Task (F(1,12)= 24.588, p< .001); significantly greater
activation was involved with preparing for an antisaccade than for a prosaccade. No main effect
of Group (F(1,12)= 1.319, p= .275) or interaction effects (F(1,12)= 1.561, p= .238) were found in
FEF during preparation. PEF had no main effect of Group (F(1,12)= .373, p= .554) but showed a
significant main effect of Task (F(1,12)= 5.794, p= .035) where significantly greater activation
was present for antisaccade preparation compared to prosaccade preparation. A significant
interaction effect between Group and Task (F(1,12)= 5.319, p= .042) during preparation was
observed in PEF, where subsequent analyses revealed that PEF had significantly greater
activation when preparing for an antisaccade compared to a prosaccade in ALS subjects only (p=
.019). DLPFC showed no main effect of Group (F(1,12)= .391, p= .545), but did display a
significant main effect of Task (F(1,12)= 12.117, p= .005); DLPFC displayed significantly more
activation when preparing for an antisaccade compared to a prosaccade. There was a trend of
an interaction effect between Group and Task (F(1,12)= 3.373, p= .093) in DLPFC that failed to
reach significance. Much like DLPFC, preparation in CD showed no main effects of Group
(F(1,12)= .026, p= .874) or interaction effects (F(1,12)= .273, p= .612). A significant main effect of
Task was seen (F(1,12)= 14.708, p= .003) such that preparation for antisaccades involved
significantly greater activation in CD than preparation for prosaccades.
62
Overall, FEF, PEF, DLPFC and CD all showed main effects of Task, where antisaccade
preparation BOLD signal change was greater than that of prosaccade preparation. PEF was the
only ROI with a significant Group-Task interaction where antisaccade preparation had stronger
beta weights than prosaccade preparation in the ALS group only. No significant Group effects
were seen between ALS and control subjects.
Saccade Execution
BOLD activations during saccade execution were investigated to determine whether the
automaticity of ALS patients’ saccade behaviour could be explained by changes in brain
activation during the execution of an eye movement. Group-level statistical maps showing
significant peak BOLD activations during prosaccade and antisaccade execution were overlaid
onto averaged anatomical images and are shown in Figure 2.8 A and B, respectively. Beta
weights taken from each ROI (threshold was lowered to extract those where p> .001) are shown
in Fig.2.9A. The mean peak beta weights shown in Fig. 2.9B were calculated for each ROI using
beta weights from the 6th and 7th time points shown in grey in Fig. 2.9A (see Methods). Talairach
coordinates of the peak voxels from the group-level statistical maps that survived cluster
correction and surpassed the significance threshold (p< .001) are listed in Table 4 for prosaccade
and antisaccade execution contrasts.
63
Pro-Exec
A
Prosaccade – Pro-catch
Z=50
ALS
Anti-Exec
B
Antisaccade – Anti-catch
Control
Z=50
Control
ALS
SEF
SEF
FEF
FEF
PEF
PEF
Y=26
Y=26
DLPFC
DLPFC
Z=10
Z=10
CD
CD
R
N=12
R
L
8.00
4.44
p < 0.001 -8.00
-4.44
t-value
N=12
L
8.00
4.44
p < 0.001 -8.00
-4.44
t-value
Figure 2.8: Saccade execution contrast maps. (A) Contrast map for pro exec created from
prosaccade trials subtracting pro-catch trials, cluster size corrected at p < .05 (10 contiguous
voxels). The 6th and 7th time points relative to trial onset were used in this subtraction. Labelled
ROIs with significant BOLD activations are shown as ‘hot’ colours. (B) Contrast map for anti exec
created from antisaccade trials subtracting anti-catch trials, cluster size corrected at p < .05 (10
contiguous voxels). The 6th and 7th time points relative to trial onset were used in this
subtraction. ROIs are labelled, with significant BOLD activations shown as ‘hot’ colours.
64
Group and
Region of
Interest
ALS
right DLPFC
left DLPFC
right FEF
left FEF
SEF
right PEF
left PEF
right CD
left CD
Control
right DLPFC
left DLPFC
right FEF
left FEF
SEF
right PEF
left PEF
right CD
left CD
Prosaccade Execution
X
18
-21
-9
12
-6
-18
9
24
-3
Y
-7
-16
-10
-76
-61
-13
-1
-67
-58
Z
t
67
58
67
40
49
Antisaccade Execution
Vol
7.62
6.00
8.39
9.15
7.93
11
21
105
169
127
58 8.27
67 8.38
49 9.68
52 14.30
34
27
183
629
X
Y
Z
t
Vol
39
-33
3
36
-30
-10
-7
-4
-46
-52
52
55
58
46
49
6.71
7.09
8.61
11.54
10.67
16
43
86
246
49
24
-27
-7
-10
55
58
6.15
7.23
31
22
30
-21
-64
-67
58
49
7.36
8.25
184
215
Table 4: Talairach coordinates (X, Y, Z) of peak activation in GLM contrast maps for prosaccade
and antisaccade execution contrasts. Coordinates are shown for significant activations only
from Fig. 2.8. DLPFC, dorsolateral prefrontal cortex; FEF, SEF, PEF, frontal, supplementary,
parietal eye fields; CD, caudate nucleus.
65
A
†
*
**
SEF
0.8
0.6
0.4
0.2
0
-0.2
0.8
0.6
†
†
0.4
0.2
0
0.8
0.6
0.4
0.2
0
-0.2
FEF
0.8
0.6
0.4
0.2
0
-0.2
PEF
0.8
0.6
0.4
0.2
0
-0.2
DLPFC
Mean Peak Beta Weight
ALS
CTRL
a p<0.05
b p<0.05
b p<0.01
BOLD Signal (% Change)
ALS Pro-Exec
ALS Anti-Exec
CTRL Pro-Exec
CTRL Anti-Exec
B
0.8
0.6
**
0.4
0.2
0
0.8
0.6
*
0.4
0.2
0
0.8
0.6
0.4
0.2
0
CD
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
-0.2
1.5 4.6 7.7 10.8 13.8 16.9 20
Pro-Exec Anti-Exec
Time (s)
1
3
5
7
9
11
13
a = significance for Group-Task interactions only
b
Time Points
= significance for main effects of Task only
(main effects of Group not shown)
Figure 2.9: Region of interest (ROI) analysis for pro and antisaccade execution. (A) Illustration
of mean BOLD signal time course for pro and anti exec trials for all ROIs. (B) Mean peak beta
weight values for the 6th and 7th time points from random effects analysis of pro exec and anti
exec trials. Beta weights were extracted from 125 cubic voxels surrounding the peak activations
in regions displaying greater activation during saccade trials compared to catch trials. Left and
right hemispheres were averaged. In ROIs where BOLD activation did not surpass the
significance threshold (p<.001), thresholds were lowered until beta weights could be obtained.
Errors bars represent standard error of the mean (SE).
66
Multiple 2 x 2 ANOVA’s were used to test for significance across the mean peak beta
weights for saccade execution that are shown in Fig. 2.9B. Only significant effects of Task or
Group-Task interactions are shown in Fig. 2.9B; significant effects of Group are not shown for
the purposes of figure clarity. A significant main effect of Group was seen in SEF (F(1,12)= 4.839,
p= .050) such that ALS patients had significantly greater activation in SEF than controls during
saccade execution. A trend approaching a main effect of Task was seen (F(1,12)= 3.287, p=.097)
but failed to reach significance. A significant interaction effect (F(1,12)= 5.560, p= .038) was
followed by subsequent analysis that revealed ALS patients had significantly greater activation in
SEF during antisaccade execution compared to prosaccade execution (p= .011). Additionally, the
ALS group had significantly stronger beta weights during the antisaccade task compared to
controls (p= .011). No significant main effect of Group (F(1,12)= .986, p= .342) or interaction
effect between Group and Task (F(1,12)= 1.241, p= .289) was found for saccade execution in
FEF. However, a significant main effect of Task (F(1,12)= 12.077, p= .005) was seen in FEF such
that antisaccades had significantly greater activation during execution than prosaccades. No
significant main effect of Group (F(1,12)= 1.813, p= .205) or interaction effect between Group
and Task (F(1,12)= .176, p= .683) was found for saccade execution in PEF. However, a significant
main effect of Task (F(1,12)= 5.903, p= .033) was seen in PEF such that antisaccade execution
was associated with significantly greater activation than prosaccade execution. Saccade
execution in DLPFC had no main effects of Group (F(1,12)= .288, p= .602), Task (F(1,12)= .605, p=
.453) or interaction effects (F(1,12)= .001, p= .977). Saccade execution in CD also displayed no
main effects of Group (F(1,12)= 1.164, p= .304), Task (F(1,12)= 1.577, p= .235) or interaction
effect (F(1,12)= .056, p= .817).
67
In summary, FEF and PEF displayed effects of Task such that antisaccades had significantly
stronger beta weights than prosaccades. ALS patients had significantly more activation in SEF
than controls in general, but more specifically, showed interaction effects where ALS subjects
had stronger beta weights in SEF than controls on the antisaccade task. Additionally, the ALS
group had significantly stronger antisaccades beta weights than prosaccades in SEF.
2.3.4 Structural Analysis
The neurodegenerative nature of ALS is known to cause brain atrophy including reductions
in white (Abrahams et al., 2005) and grey matter (Chang et al., 2005; Murphy et al., 2007b)
within motor, premotor and frontotemporal regions, in addition to enlarged ventricles (Frank et
al., 1997). To investigate whether such changes could be seen within this ALS group, cortical
thickness was contrasted between controls and ALS subjects, and is displayed in Fig. 2.10 in
terms of the level of significance (q values), rather than in mm of thickness. Areas displaying
‘hot’ colours signify brain regions that had decreased cortical thickness in the ALS brain
compared to age-matched controls, and these trends are approaching statistical significance (q’s
range 0.16 - 0.12).
68
FEF
FEF
SEF
SEF
PEF
DLPFC
N = 16
Figure 2.10: Significance level of cortical thickness. Cortical thickness in terms of grey matter
(GM) compared across ALS and control groups, where ‘hot’ colours represent decreased GM in
the ALS brain. Multiple comparisons were corrected for using false discovery rate correction.
ROIs overlapping with areas of thinning cortex are labelled. Significant GM thinning is defined by
q<.05.
Volumetric estimations of differences in cerebrospinal fluid (CSF; t(30)= .087, p= .931), grey
matter (GM; t(30)= -1.080, p= .289) and white matter (WM; t(30)= .986, p= .332) between ALS
and controls are shown in Fig. 2.11A, however no statistically significant differences between
groups were found. Further investigations were conducted despite the apparent similarities
between ALS and control groups in terms of GM volume to determine whether loss of GM
followed a pattern typical of frontotemporal dementia (FTD), and whether GM volume
69
correlated to any behavioural parameters. Estimations of GM volume across the lobes of the
brain and cerebellum are shown in Fig. 2.11B. No significant differences were seen between ALS
and control GM in the parietal lobes (t(30)= -1.333, p= .193), temporal lobes (t(30)= -.618, p=
.541 ), occipital lobes (t(30)= -.737, p= .467), or cerebellum (t(30)= .050, p= .961). However, a
trend of decreased GM in the ALS brain was apparent in the frontal lobes (t(30)= -1.703, p=
.099). GM volume was also investigated in more specified areas of each lobe as illustrated in Fig.
2.11C. A trend approaching significance was found in medial frontal (t(30)= -1.868, p= .072) and
medial temporal cortices (t(30)= -2.009, p= .054) suggesting ALS may have decreased GM
compared to controls in these areas. GM volume in orbitofrontal (t(30)= -.190, p= .850), lateral
prefrontal (t(30)= -1.514, p= .141) and lateral temporal cortices (t(30)= -.427, p= .672) showed
no significant difference across groups.
70
90
80
70
60
50
40
30
20
10
0
GM Volume (x103 mm3)
Volume (x104 mm3)
◊
B
A
75
60
45
30
15
0
CSF
GM
WM
Frontal
Parietal
Temporal Occipital
Cerebellum
N=16
ALS
Control
†
◊
p<0.05
p<0.1
GM Volume (x103 mm3)
C
70
60
50
40
30
◊
◊
20
10
0
Medial
Frontal
Orbitofrontal
Lateral
Prefrontal
Medial
Lateral
Temporal Temporal
Figure 2.11: Volumetric analysis. (A) Volumetric estimations of cerebrospinal fluid (CSF), grey
matter (GM) and white matter (WM). (B) Volumetric estimations of GM in the frontal, parietal,
temporal, and occipital lobes and cerebellum. (C) Grey matter volume estimations in the medial
frontal, orbitofrontal, lateral prefrontal, medial temporal, and lateral temporal areas of the
brain. Error bars represent standard error of the mean (SE).
2.3.5 Correlation Analysis
Saccade performance vs. Neuropsychological scores
Many of the tests administered as part of the neuropsychological battery were sensitive to
identifying executive dysfunction, a measure of frontal lobe functioning which can also be tested
using the antisaccade task (Hallett, 1978; Munoz & Everling, 2004). Performance on the
antisaccade task in terms of SRT and direction errors relies on the intactness of frontal lobe
cortical areas which control executive functions (Munoz & Everling, 2004; Pierrot-Deseilligny et
71
al., 1991a; Pierrot-Deseilligny et al., 2003; Rivaud et al., 1994). For this reason, antisaccade SRTs
and direction errors were correlated with neuropsychological test scores from Table 1. All
correlations of antisaccade SRTs were found to be not significant (p’s> .111) (Figure not shown).
All correlations of antisaccade direction errors were also found to be not significant (p’s> .062)
(Figure not shown), with the exception of the MoCA score which displayed a significant
correlation with antisaccade direction errors (r(12)= -.686, p= .010) (Fig. 2.3E), as reported in
Section 2.3.2.
Peak beta weights vs. Saccade performance
The brain regions selected a priori for our ROIs are known to be critically involved with
generating voluntary saccades; their proper functioning establishes the preparatory set that
dictates saccade performance in terms of SRT and direction errors (Connolly et al., 2002;
DeSouza et al., 2003; Pierrot-Deseilligny et al., 1991a; Pierrot-Deseilligny et al., 2002; PierrotDeseilligny et al., 2003). Therefore, mean peak beta weights from each ROI in the antisaccade
preparation and execution contrasts were correlated with measures of antisaccade performance
such as SRT and proportion of direction errors for both ALS and control groups. Significant
correlations only are shown in Fig. 2.12, where data points represent the mean antisaccade SRT
for each subject intersecting the mean peak beta weight for that same subject.
When beta weights from antisaccade preparation were correlated with antisaccade SRT,
negative correlations were observed across all ROIs (Fig. 2.12A); meaning stronger beta weights
were associated with faster SRTs. In particular, PEF beta weights had a significant negative
correlation with antisaccade SRT in the control group (r(12)= -.766, p= .004), but not the ALS
group (r(12)= -.447, p= .145). SEF beta weights showed a significant negative correlation with
72
antisaccade SRT in the ALS (r(12)=-.594, p= .042), but not control groups (r(12)= -.229, p= .475).
Antisaccade preparation beta weights from CD correlated significantly with antisaccade SRTs in
the ALS group (r(12)= -.575, p= .050), but not in controls (r(12)= -.076, p= .814). DLPFC beta
weights correlated significantly with antisaccade SRTs in the ALS group (r(12)= -.583, p= .047),
but not in controls (r(12)= -.408, p= .187). The final ROI whose beta weights showed a significant
negative correlation with antisaccade SRT in the control group was FEF (r(12)= -.676, p= .016),
however no correlation was seen in the ALS group (r(12)= -.440, p=.152). No significant
correlations were found between antisaccade preparation beta weights and antisaccade error
rates in the ALS group (p’s> .302) or controls (p’s> .232) (Figures not shown).
Peak beta weights from antisaccade execution were also correlated with antisaccade SRTs
across all ROIs, however only PEF showed a significant positive correlation in the ALS group
(r(12)= .603, p= .038), but not for control subjects (r(12)= .017, p= .958) (Fig. 2.12B). Meanwhile,
all other ROIs showed no significant correlation in ALS (p’s> .111) or controls (p’s> .101) (Figures
not shown). Antisaccade execution beta weights also showed no significant correlation to the
rate of antisaccade direction errors made in the ALS group (p’s> .209), or controls (p’s> .055)
with the exception of DLPFC. DLPFC anti execution beta weights showed a significant positive
correlation with anti error rates in the ALS group (r(12)= .596, p= .041) (Fig. 2.12B) such that
stronger beta weights were associated with more direction errors; meanwhile controls showed
no correlation (r(12)= .394, p= .206).
73
Antisaccade Preparation
Antisaccade SRT(ms)
600
r = -0.447
r = -0.766
SEF
‡
600
500
400
400
300
300
200
200
0 0.2 0.4 0.6 0.8 1 1.2
CD
B
PEF
r = -0.575
r = -0.076
†
†
‡
0
600
r = -0.583
r = -0.408
†
FEF
600
500
500
500
400
400
400
300
300
300
200
-0.2 0 0.2 0.4 0.6 0.8 1
200
200
0
0
ALS
Control
p<0.05
p<0.005
0.2 0.4 0.6 0.8 1
DLPFC
Antisaccade Execution
†
N =12
500
600
r = -0.594
r = -0.229
Antisaccade SRT(ms)
PEF
0.2 0.4 0.6 0.8 1
r = -0.440
r = -0.676
†
0.2 0.4 0.6 0.8 1
600
r = 0.603
r = 0.017
†
500
400
300
200
0 0.2 0.4 0.6 0.8 1 1.2
DLPFC
Direction Errors (%)
A
100
r = 0.596
r = 0.394
†
80
60
40
20
0
-0.2
0
0.2
0.4
0.6
Mean Peak Beta Weights
Figure 2.12: Correlations between antisaccade measures (SRT and direction errors) and mean
peak beta weights during anti preparation and execution. (A) Mean peak beta weights were
extracted from the 5th and 6th time points of random effects analysis for anti prep trials from the
125 cubic voxels surrounding the peak activations in regions displaying greater activation during
anti-catch trials compared to fixation trials. (B) Mean peak beta weights were extracted from
the 6th and 7th time points of random effects analysis for anti exec trials from the 125 cubic
voxels surrounding the peak activations in regions displaying greater activation during
antisaccade trials compared to anti-catch trials.
An additional correlation was performed to determine whether the degree of preparatory
activation for prosaccade trials was linked to the significantly increased percentage of express
saccades made on prosaccade trials by ALS patients. However, no significant correlations were
found between prosaccade preparation beta weights from any ROIs and the percentage of
express saccades in ALS (p’s> .349) or controls (p’s> .097) (Non-significant correlations not
shown in Figures).
74
Peak beta weights vs. Neuropsychological scores
Because MoCA scores were the only neuropsychological test displaying a significant
correlation with antisaccade direction errors made by ALS subjects (Fig. 2.3E), and direction
errors showed significant correlations with some ROI peak beta weights in ALS, MoCA scores
were therefore the only psychometric test chosen to be correlated with mean peak beta
weights from saccade preparation and execution. However, no significant correlations were
found between MoCA scores and pro- or anti-saccade preparation and execution beta weights
(p’s> .243) (Non-significant correlations not shown in Figures).
Peak beta weights vs. Grey matter volumes
Because BOLD activation was measured from grey matter, changes in the degree of brain
activation in ALS may be attributed to structural degeneration of grey matter. Peak beta weights
from all ROIs were correlated with some of the grey matter volumes shown in Fig. 2.11B and C
using two-tailed Pearson correlations. Peak beta weights from contrasts of interest (pro prep,
pro exec, anti prep, and anti exec) were correlated with grey matter volumes that most closely
overlapped with each ROI (Fig. 2.13); frontal lobe GM was correlated against all contrasts of
interest for DLPFC and FEF, medial frontal GM was correlated with SEF activation, parietal GM
was correlated with PEF activation, and lateral prefrontal GM was correlated with DLPFC
activation.
75
A
Anti Preparation
LPF GM Volume (x103 mm3)
DLPFC
r = 0.044
r = 0.603
60
DLPFC
†
60
50
50
40
40
30
30
20
0
0.2 0.4 0.6 0.8 1
B
ALS
Control
p<0.05
p<0.005
20
-0.2
0
r = -0.649
r = -0.357
0.2
0.4
†
0.6
Pro Execution
PL GM Volume (x103 mm3)
N =12
†
‡
Anti Execution
PEF
r = -0.727
r = 0.304
†
70
60
50
40
30
20
0
0.2
0.4
0.6
0.8
Mean Peak Beta Weights
Figure 2.13: Correlations between grey matter (GM) volume and peak beta weights. (A) GM
volume estimations from lateral prefrontal cortex (LPF) were correlated with mean peak beta
weights of DLPFC extracted from the 5th and 6th time points for anti prep and the 6th and 7th time
points for anti exec from the 125 cubic voxels surrounding the peak activation. (B) GM volume
estimations from the parietal lobes (PL) were correlated with mean peak beta weights of PEF
from the 6th and 7th time points of prosaccade execution trials from the 125 cubic voxels
surrounding the peak activation where greater activation was seen during prosaccade trials
compared to pro-catch trials. Right and left hemispheres were averaged.
The volume of frontal lobe GM was first correlated with prosaccade preparation and
execution beta weights for both DLPFC and FEF, however none of these correlations were
significant in the ALS (p’s> .382) or control groups (p’s> .290). Frontal lobe GM was then
correlated with antisaccade preparation and execution in FEF which resulted in no significant
76
findings in ALS (p’s> .707) or control groups (p’s> .262). However, anti preparation beta weights
from DLPFC correlated significantly with frontal lobe GM in controls (r(12)= .693 , p= .012) such
that larger GM volume was associated with stronger beta weights; meanwhile no correlation
was found in ALS subjects (r(12)= .086, p= .790) (Figure not shown). DLPFC antisaccade
execution beta weights showed a significant negative correlation with ALS frontal lobe GM
volumes (r(12)= -.644, p= .024), where stronger beta weights were associated with less GM
volume, but no significant correlation was seen in controls (r(12)= -.399, p= .199) (Figures not
shown).
DLPFC was also correlated with a more specific measure of GM volume, that of the lateral
prefrontal cortex, which more closely represents the DLPFC ROI than the estimated GM volume
of the entire frontal lobes. For this reason, only the lateral prefrontal cortex correlations with
DLPFC are shown in Fig. 2.13. DLPFC anti preparation beta weights of control subjects
demonstrated a significant positive correlation with lateral prefrontal GM (r(12)= .603, p= .038),
but ALS beta weights did not (r(12)= .044, p= .893). GM of lateral prefrontal cortex displayed a
significant negative correlation with ALS anti execution beta weights (r(12)= -.649, p= .022) such
that stronger activation occurred with less GM, but did not correlate with control beta weights
(r(12)= -.357, p= .255).
No significant correlations were found between GM volume of medial frontal cortex and SEF
for any of the contrasts of interest in ALS (p’s> .058) or control groups (p’s> .258). The only
significant correlation found between PEF beta weights and parietal lobe GM volumes was in
the ALS group for anti execution contrasts (r(12)= -.727, p= .007); meanwhile controls showed
no correlation (r(12)= .304, p= .337). All other contrasts of PEF beta weights (pro prep, anti prep,
77
anti exec) showed no significant correlation with parietal lobe GM volumes in ALS (p’s> .063) or
control groups (p’s> .072).
2.4 Discussion
The objective of this study was to investigate the neural correlates of cognitive impairment
in ALS: 1) using a battery of neuropsychological tests to detect subtle cognitive and behavioural
deficits related to executive function; 2) using measures of behavioural performance on
voluntary saccadic eye movement tasks controlled by frontal regions of the brain; 3) using
functional imaging to determine if ALS patients showed significant differences in activation in
oculomotor brain regions compared to controls and whether this influenced their flexible
control of saccade behaviour, and 4) to determine if the pattern of extramotor cortical
degeneration in ALS overlaps with areas that are involved with controlling saccades and
executive functions. We found that, compared to controls, the ALS patients displayed signs of
cognitive impairment, and were biased towards automated responses as demonstrated by their
inclination towards making more express saccades and their increased rate of direction errors
on antisaccade trials.
Due to physical restrictions associated with imaging ALS patients, only a subset completed
the imaging portion of the protocol; this lack of statistical power left us mostly with trends to
interpret our data. Functionally, there were trends in the ALS group that suggested decreased
preparatory activation in all ROIs for prosaccades, and increased activation for antisaccade
preparation compared to controls, which suggests the inability to establish the appropriate task
set perhaps as a result of executive dysfunction. A trend of widespread cortical degeneration
was seen in the ALS patients, and overlapped with oculomotor and executive brain areas. We
78
suggest that the pattern of cortical thinning in oculomotor and executive control regions may
contribute to the cognitive impairment seen in ALS patients, which may cause them to be more
prone to distractibility, as shown by their automatic saccade behaviour and their difficulty
establishing the appropriate task set in terms of preparatory brain activation.
2.4.1 Influence of Cognitive Impairment
As a group, ALS patients varied considerably in their cognitive performance on
neuropsychological evaluations, suggesting a continuum of impairments in this small sample size
(Table 1). However, on average, we found that ALS patients displayed mild cognitive
impairment, based on their poor performance on the MoCA and verbal fluency evaluations, in
addition to behavioural fluctuations, as demonstrated by their borderline emotional lability.
Based on previous findings, we can assume that poor performance on the MoCA and verbal
fluency assessments are likely a result of ALS patients displaying impairments in executive
function, working memory, attention and concentration (Abrahams et al., 2000; Nasreddine et
al., 2005).
We suspect that ALS patients with labile affect, which is presumed to result from damage to
the brainstem and/or frontotemporal regions (Moore et al., 1997; Poeck, 1969), could represent
a subset of patients with frontotemporal dementia (FTD)-like pathology (Merrilees et al., 2010;
Neary et al., 1998; Neary et al., 2000; Strong et al., 1999; Talbot et al., 1995; Wilson et al., 2001).
A large body of evidence supports a continuum between FTLD and ALS, in which deficits of
varying severity in behavioural conduct and executive control are characteristic (Abe et al.,
1997; Heidler‐Gary & Hillis, 2007; Merrilees et al., 2010; Neary et al., 1990; Neary et al., 1998;
Neary et al., 2000; Ringholz et al., 2005; Strong et al., 1999).
79
Both verbal fluency and emotional lability deficits appear relatively early on in ALS and
remain consistent over time even in non-demented patients (Abrahams et al., 2005), which
could explain why we were able to detect these changes in our small sample that mostly
consisted of relatively new diagnoses. It is possible that other significant deficits would have
been identified in other cognitive domains if this group of patients had been more progressed in
their disease, as previous studies found worsening cognitive performance as ALS progressed
(Strong et al., 1999).
Although no other psychometric tests in our study revealed any abnormalities of ALS
subjects as a group, individually, some patients had worse cognitive profiles than others. In
supplementary analyses, it was revealed that a number of neuropsychological test scores
correlated significantly with one another (Table 5). For example, patients who performed well
on the MoCA had better judgment and reasoning scores (as shown by the positive correlations),
were better at word generation (N.B., low VF scores are associated with better word
generation), and had fewer depressive tendencies (as shown by the negative correlations).
Numerous other correlations were significant, but in general, the patients who showed signs of
impairment in one area tended to perform poorly on other cognitive tests as well. This suggests
a pattern of overall cognitive and behavioural impairment that is reminiscent of the ALS-FTD
spectrum (Neary et al., 1998), in that it affects a broad range of top-down processes.
80
N = 21
MoCA
MoCA
1
0.677**
Judgment
0.677**
Reasoning
Judgment Reasoning
VF
FBI
CNS-LS
Anxiety Depression
0.667**
-0.584**
-0.193
0.128
0
-0.499*
1
0.616**
-0.414
0.113
-0.159
0.227
-0.397
0.667**
0.616**
1
-0.665**
-0.261
0.151
-0.081
-0.303
VF
-0.584**
-0.414
-0.665**
1
0.039
-0.492*
-0.141
0.39
FBI
-0.193
0.113
-0.261
0.039
1
0.161
0.217
0.122
CNS-LS
0.128
-0.159
0.151
-0.492*
0.161
1
0.244
-0.005
Anxiety
0
0.227
-0.081
-0.141
0.217
0.244
1
-0.008
Depression
-0.499*
-0.397
-0.303
0.39
0.122
-0.005
-0.008
1
VF = verbal fluency, FBI = Frontal Behavioural Inventory, CNS-LS = Centre for Neurologic
Studies Lability Scale, ** = p< 0.01, *= p< 0.05
Table 5: Pearson correlations between neuropsychological evaluations.
Out of the cognitive domains tested, the common thread between successful performance
on the MoCA, COWA and CNS-LS can be attributed to successful top-down processing, such as
intact executive function, attentional control, and response inhibition. Because ALS patients did
not perform well on these measures, we can assume that their top-down processes are not
performing optimally. We therefore investigated their performance on saccade tasks that were
designed to probe these executive functions.
2.4.2 Saccades
Behaviourally, there was large variability in our data, especially within the ALS group. This
variability was likely introduced because disturbances to eye movements in ALS are thought to
lie on a continuum, much like the proposed continuum of cognitive impairment in ALS, where
81
normal eye movements would exist at one end of the spectrum, while the uncommon
occurrence of ophthalmoplegia (or paralysis of the extraocular muscles) in highly progressed
patients would exist at the other end of the spectrum (Averbuch-Heller et al., 1998; Donaghy et
al., 2011). Despite this variability, clear trends emerged from our small sample size.
In general, antisaccade SRTs were longer than prosaccade SRTs (Fig. 2.3A), and more
direction errors were made on antisaccade compared to prosaccade trials (Fig. 2.3D). This
behaviour is believed to reflect the additional executive processing required of the antisaccade
task (e.g., inhibition and top-down attentional control) (Munoz & Everling, 2004). Differences
between ALS and control groups for prosaccade and antisaccade mean SRTs and direction errors
did not reach significance, but did reveal a trend that is consistent with DLPFC and FEF damage
or dysfunction (Guitton et al., 1985; Pierrot-Deseilligny et al., 1991a; Pierrot-Deseilligny et al.,
2002; Pierrot-Deseilligny et al., 2003; Rivaud et al., 1994). ALS patients had somewhat longer
antisaccade SRTs than controls (Fig. 2.3A), and slightly elevated antisaccade error rates (Fig.
2.3D), both of which have been previously demonstrated in ALS (Donaghy et al., 2010a; Donaghy
et al., 2010b; Donaghy et al., 2011; Evdokimidis et al., 2002; Shaunak et al., 1995). The ALS
patients’ impaired performance on producing correct antisaccades cannot be attributed simply
to a lack of understanding the task, because antisaccade errors were corrected in all patients.
This suggests that they understood the task instructions, but were simply unable to suppress the
visual grasp reflex in order to prevent erroneous prosaccades on antisaccade trials, perhaps due
to insufficient inhibition from higher level cortical areas such as DLPFC (Guitton et al., 1985).
Because no group effects were observed for mean SRT, it is possible that this measure alone
was not sensitive enough for detecting differences between the ALS and control groups.
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However, express saccades appeared to provide a more sensitive measure; ALS patients made
significantly more express saccades than controls on prosaccade trials (Fig. 2.3C). Express
saccades have not been explicitly studied in ALS before, but the rate at which these ALS subjects
were making express saccades was quite striking. Based on neural recordings in the monkey,
express saccades are elicited when high levels of pre-target activity combine with visual
responses in saccade-related neurons of the SC (Dorris et al., 1997; Dorris & Munoz, 1998;
Everling et al., 1998; Everling et al., 1999). During the gap period, pre-target preparatory activity
is elevated and conditions are optimal for express saccade generation, making suppression of an
unwanted saccade on an antisaccade trial very difficult, unless sufficient inhibition from the
frontal lobes were to be exerted on saccade neurons in SC (Everling et al., 1998; Everling et al.,
1999; Everling & Munoz, 2000). Therefore, the ALS patients’ bias towards automatic express
saccades may have stemmed from high levels of motor preparation activity, which when
combined with poor executive control, likely impeded their ability to suppress the automatic
response when performing the antisaccade task.
Interestingly, the ALS group showed a trend of making slightly fewer direction errors on
prosaccade trials than controls (Fig. 2.3D). Other studies that have examined automatic saccade
behaviour in ALS have failed to find differences between patient and control groups (AverbuchHeller et al., 1998; Evdokimidis et al., 2002; Shaunak et al., 1995), with the exception of bulbaronset ALS patients, who displayed longer prosaccade SRTs than spinal-onset ALS patients and
controls (Donaghy et al., 2010a). It is possible that bulbar-onset patients had greater saccadic
impairments due to the greater pathological involvement of the brainstem, which houses key
oculomotor structures (Scudder et al., 2002; Sparks, 2002). In the current study however,
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patients from whom we acquired behavioural data were all spinal-onset cases, and they
performed better than control subjects on the prosaccade task. This is the behaviour we would
expect if ALS patients had high levels of preparatory activation related to motor planning during
the prosaccade task, because it would be highly unlikely for them to exert sufficient top-down
inhibition to suppress the prosaccade to instead produce an erroneous antisaccade. Rather, it is
more likely that the decreased proportion of errors made by ALS patients on the prosaccade
task was due to their inclination towards the visual grasp reflex. Control subjects may have
occasionally made anticipatory errors where they predicted which side of the screen the target
would appear, rather than simply responding to the target with a visual grasp.
Both ALS and control groups had significantly more variable reaction times for prosaccades
than antisaccades, as determined by CVSRT (Fig. 2.3B). High intrasubject variability is common
within aging and clinical populations (Cameron et al., 2011; Peltsch, 2011); however,
antisaccade SRTs were more consistent across ALS and controls, meanwhile prosaccade SRTs
were significantly more variable in ALS patients. This discrepancy between tasks is likely due to
the more complex processing steps required for the antisaccade task, which would produce a
smaller range of feasible reaction times (Munoz & Everling, 2004). In contrast, planning of a
prosaccade may take a number of oculomotor pathways, some of which are more direct than
others and could make the reaction times more variable. The significantly higher percentage of
express saccades made by ALS patients than controls may have been driving the significantly
greater variability we observed with ALS prosaccade CVSRT.
ALS patients who failed to suppress the visual grasp reflex during antisaccade trials also
performed poorly on the MoCA (Fig. 2.3E). Antisaccade direction errors in ALS are often
84
associated with ‘distractibility’ and are thought to reflect a lack of executive and attentional
control, and abnormal scores on tests of executive function have previously been shown to
correlate with distractibility in ALS (Donaghy et al., 2010a; Evdokimidis et al., 2002). We
postulated that the behaviour displayed by ALS patients was characteristic of distractibility,
likely as a result of impaired executive functioning, and that neuroimaging techniques would
detect differences in function of oculomotor brain areas.
2.4.3 Neural Circuitry
We hypothesized that the automatic nature of the ALS patient’s saccadic responses,
primarily characterised by a higher proportion of express saccades, may be associated with
greater preparatory activity related to motor planning in the SC. Therefore, we investigated the
functionality of regions within the frontal, parietal and subcortical areas that are known to
mediate activity in the SC (Everling & Munoz, 2000; Hikosaka et al., 2000; Hikosaka et al., 2006;
Huerta et al., 1986; Huerta & Kaas, 1990; Paré & Wurtz, 2001), to examine how the functioning
of this neural circuitry may explain saccade behaviour. More specifically, we wanted to establish
whether differences in activation, in terms of BOLD signal change across Task and Group, could
be attributed to differences in saccade preparation or execution.
Our fMRI analyses revealed that in FEF, PEF, DLPFC and CD, significantly greater preparatory
BOLD activation was recorded for antisaccades compared to prosaccades in general, and was
consistent with previous findings (Brown et al., 2007; Connolly et al., 2005; Curtis & D'Esposito,
2003b; Curtis & Connolly, 2008; DeSouza et al., 2003; Ford et al., 2005; Luna et al., 1998;
Sweeney et al., 1996). This difference in BOLD activation is thought to reflect the differences in
processing of information (i.e., suppressing automatic responses) (Munoz & Everling, 2004)
85
and/or differences in difficulty (i.e., attentional load) (Kimmig et al., 2001) between the two
saccade tasks. SEF was the only ROI that did not have significant differences in activation
between the two tasks; however, upon examination of these findings, it appears that this was
driven by the control subjects who showed little difference in activation between prosaccade
and antisaccade trials, rather than the ALS subjects.
BOLD signal is thought to reflect the input and processing of information from inhibitory and
excitatory neural projections from other regions (Logothetis, 2003; Logothetis & Wandell, 2004).
Because ALS patients had significantly greater preparatory activation in PEF for antisaccades
compared to prosaccades, we can speculate that increased BOLD signal change in PEF may be
related to increased input from other areas such as FEF, SEF, and DLPFC (Connolly et al., 2002;
DeSouza et al., 2003; Pierrot-Deseilligny et al., 2002). This increased input from other ROIs might
serve to prevent prosaccade generation via suppression of parietal activity (Pierrot-Deseilligny
et al., 1991a).
The high frequency with which ALS patients produced express saccades may be explained by
preparatory activation in PEF. Increased parietal signalling during the first half of the gap period
has been shown to predict PEF activation in the second half of the gap, where greater activity
increased express saccade probability (Hamm, Dyckman, Ethridge, McDowell, & Clementz,
2010). This presents an apparent incongruence between our findings of slightly decreased
prosaccade preparatory activation in PEF within the ALS compared to controls, despite the ALS
patients making substantially more express saccades. The discrepancy between our findings and
those of Hamm et al. (2010) may be explained by: 1) the relatively poor temporal resolution of
BOLD fMRI, where gap-related activation cannot be specifically looked at, compared to the
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superior resolution that was achieved with EEG by Hamm and colleagues; 2) the fact that we did
not explicitly model express saccades, and therefore the prosaccade BOLD response for ALS
patients reflects all prosaccade latencies; and 3) the mean peak beta weights upon which we
based our statistical analyses were taken from time points that corresponded to the peak of the
hemodynamic response for all prosaccade latencies, which might not correspond to the
appropriate hemodynamic response for the express saccade epoch. It is possible that
performing sub-analyses by modelling express saccades into our fMRI data in the future might
allow us to pull out differences in the mean peak hemodynamic response between ALS and
controls associated with express saccade preparation only. Alternatively, including more
functional imaging data by collecting more patients might alter our findings, such that PEF
preparatory activation might be greater in ALS patients than controls.
Differences in BOLD signal change between ALS and control groups did not reach statistical
significance for saccade preparation. However, trends of slightly decreased prosaccade and
slightly increased antisaccade preparatory activation in ALS patients compared to controls were
observed in all ROIs. To our knowledge, there is an absence of research investigating differences
between ALS and control subjects in terms of cortical activation during the antisaccade and
prosaccade tasks. This raises an important research question: whether our paradigm using BOLD
fMRI provides a sensitive enough measure for detecting changes to brain function in an ALS
population that are relatively not progressed. Perhaps in a more progressed group of patients,
or if a larger sample size were to be collected, we would find significant differences in BOLD
signal change between ALS and controls.
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When interpreting the trend of decreased prosaccade preparatory activation that we found
in ALS patients compared to controls, we must consider how changes in cerebral perfusion (in
terms of regional cerebral blood flow; rCBF) might influence the BOLD signal measured in ALS
patients. Decreased rCBF in extramotor areas including frontal, temporal, and parietal regions
has been well documented in ALS populations, where greater reductions in perfusion were
found in patients displaying cognitive impairment (Abrahams et al., 1996; Abrahams et al., 2004;
Kew et al., 1993a; Talbot et al., 1995). However, we cannot attribute decreased BOLD signal
change in ALS during prosaccade preparation to reduced rCBF because ALS patients showed
somewhat increased activation during antisaccades. This indicates that these differences
between ALS and controls are unlikely a result of a global decrease in perfusion, since BOLD
signal varied with the task at hand. Conversely, insufficient forced vital capacity (FVC %
predicted) can be neglected as a potential reason for reduced BOLD signal change in the ROIs,
because all patients were screened based on a FVC of 60% or higher in order to take part in the
MRI experiment.
Another possible explanation for why we did not find significant differences between ALS
and controls in these ROIs could be a result of cortical reorganization (for review see: (Lulé,
Ludolph, & Kassubek, 2009)). There has even been evidence of shifts in BOLD signal activity in
ALS compared to controls, where ALS patients recruited more anterior areas of the pre-motor
cortex when performing upper limb movement tasks (Konrad et al., 2002). Despite findings that
cortical reorganization is related to upper motor neuron pathology (Tessitore et al., 2006), this
neural plasticity is not necessarily unique to ALS; evidence of anterior shifts in activation have
been shown in patients with other neurological conditions, such as stroke, to compensate for
88
loss in motor function (Weiller, May, Sach, Buhmann, & Rijntjes, 2006). If reorganization
occurred within this group of ALS patients, then perhaps peak BOLD activations would lie
outside of the ROI locations that were chosen a priori, thereby explaining why our ROI analyses
were inconclusive in terms of identifying group differences in activation. There is doubt,
however, as to whether frontal lobe functions can undergo reorganization; it has been
suggested that areas of executive control do not have the same redundancy as motor areas,
thereby making compensation unlikely (Lulé et al., 2009). Therefore, it seems improbable that
areas such as DLPFC were shifted in terms of the peak activations during the cognitively
demanding antisaccade task in the ALS brain compared to controls.
We found significantly greater activation in FEF and PEF during antisaccade execution
compared to prosaccades in general (Fig. 2.9B). Previous findings suggested that increased
activation in FEF and PEF indicates they are involved with generating antisaccades (Ettinger et
al., 2008), likely through their projections to SC (Leigh & Zee, 1999). Interestingly, increased
activity in the monkey lateral intra-parietal area (which corresponds to the human PEF) has been
recorded during antisaccades compared to prosaccades (Gottlieb & Goldberg, 1999). However,
it is not known whether antisaccade-related activation in parietal areas reflects activity related
to saccade generation, or whether it represents visual attention and/or visuospatial remapping
of the target location for a saccade response (Brown, Goltz, Vilis, Ford, & Everling, 2006).
We did not anticipate finding differences between ALS and control subjects in terms of
BOLD signal change during saccade execution, because there were no significant differences in
saccade metrics between groups in terms of amplitude and velocity for pro- and anti-saccades.
Surprisingly, we found that ALS patients, but not controls, showed significantly greater
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activation in SEF during execution of antisaccades compared to prosaccades. SEF’s involvement
during antisaccade execution has been demonstrated previously in human imaging studies
(Ettinger et al., 2008). SEF is thought to be mostly involved with the execution of volitional
saccades, such as antisaccades (Coe et al., 2002; Curtis & D'Esposito, 2003b), and pre-learned
saccade sequences, which are believed to rely on the same neural resources as voluntary
saccades (Petit et al., 1996). Neural recordings in nonhuman primates also support these
findings, where SEF neurons discharged more often during onset of antisaccades than
prosaccades (Schlag-Rey & Amador, 1997).
Greater activations in dorsal oculomotor areas such as PEF, FEF, and SEF have been
associated with tasks requiring greater attentional demand, such as newly learned saccade
sequences (Grosbras et al., 2001), or in this case, greater activation was associated with the
antisaccade task. Therefore, the significantly greater antisaccade BOLD signal change in SEF
during antisaccade execution might reflect a higher attentional demand required by the ALS
patients than the controls. This may be signifying greater task difficulty, rather than genuine
changes in functional performance, since ALS patients and controls at resting state previously
showed no differences in fMRI activation patterns (Mohammadi et al., 2009). Greater activation
during saccade execution in ALS has been found in areas associated with motor learning, such as
the basal ganglia, cerebellum (Han & Ma, 2006; Konrad et al., 2002) and brainstem (Konrad et
al., 2002). Our study did not find significant differences in CD during saccade execution, and we
did not explicitly image the brainstem and cerebellum. It is assumed that changes to BG function
in ALS are related to a loss of predominantly UMNs (Tessitore et al., 2006), but most patients in
our study displayed both upper and lower motor neuron deficits.
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In summary, while fMRI neuroimaging provides a valuable clinical tool for investigating
neural changes in ALS, it remains speculative as to whether changes in brain function during
saccade tasks are sensitive enough to provide any diagnostic value, or whether these changes
represent individual phenomena that vary between subjects. We predict that increasing the
sample size of our functional data might make this measure of brain function more sensitive to
differences between neurodegenerative and healthy groups.
2.4.4 Structural Integrity
There has been substantial evidence pointing to abnormalities in brain regions that lie
outside of the motor cortex in ALS, where cognitively impaired patients displayed greater
extramotor involvement (Abrahams et al., 1996; Abrahams et al., 2000; Abrahams et al., 2005;
Chang et al., 2005; Guedj et al., 2007; Neary et al., 2000). We found trends of widespread loss of
cortical thickness in terms of GM in ALS patients compared to age-matched controls which
appeared to overlap with many of the oculomotor ROIs (Fig. 2.10). Changes in cortical volume in
frontal and temporal regions have been said to underlie changes in brain function in ALS
patients without cognitive impairments (Abrahams et al., 2003). Therefore, these structural
changes may influence the differences we previously described in functional and behavioural
performance on oculomotor tasks in the ALS group compared to controls.
Despite previously recorded differences between ALS and controls on measures of
cerebrospinal fluid (CSF), grey matter (GM) or white matter (WM) volume (Abrahams et al.,
2005; Chang et al., 2005; Frank et al., 1997; Murphy et al., 2007b; Thivard et al., 2007), we did
not find significant differences between groups (Fig. 2.11A). It is possible that differences in GM,
WM and CSF would arise if we separated ALS patients who displayed more severe cognitive
91
impairment in our sample from those who were less impaired, since greater atrophy has been
documented in ALS patients with comorbid cognitive abnormalities (Murphy et al., 2007a;
Murphy et al., 2007b).
We also found trends of extramotor atrophy in ALS patients in terms of decreased GM
volume in frontal, medial frontal, and medial temporal regions (Figs. 2.11B, C). This appeared to
follow a frontotemporal pattern described previously (Kato et al., 1993; Yoshida, 2004), which is
believed to spread with disease progression and is a hallmark of frontotemporal dementia (FTD)
(Neary et al., 1998). Even though our patients did not strictly meet the diagnostic criteria for
FTD, they did display a continuum of impairments based on neuropsychological tests that are
consistent with impaired frontal lobe functions. We can therefore infer that worsened cognitive
function, more severe disease progression, and loss of structural integrity are highly
interconnected. It is possible that if our sample contained more highly progressed patients, this
atrophy would extend from anterior to posterior areas, and may reach statistically significant
group differences.
As such, our structural data show promising preliminary findings of frontotemporal atrophy;
however it remains unclear as to whether the behavioural and functional changes we observed
in ALS can be attributed directly to pathological structural changes, or whether they represent
functional compensation as a result of increased task difficulty.
2.4.5 Functional Compensation
Cortical degeneration can result in various compensatory mechanisms that make up for
motor function loss in ALS, such as recruitment of surrounding cortical areas and even cortical
reorganization (Lulé et al., 2009). Our functional results from oculomotor ROIs can be looked at
92
in the context of functional compensation, and we propose potentially two compensation
mechanisms were at work: compensation due to task difficulty vs. structural damage.
Behaviourally, it appears as though the antisaccade task was considerably more difficult
than the prosaccade task for the ALS patients, where greater difficulty would require more
cognitive effort exerted to perform the task correctly (Schoenfeld et al., 2005). It has been
suggested that functional compensation in ALS may be achieved via recruitment of existing
cortical resources to make up for the additional task difficulty (Schoenfeld et al., 2005), which
would explain the trends we saw of increased BOLD signal change for antisaccade preparation.
One could also argue that difficulty with the antisaccade task in ALS patients was derived from
GM loss in oculomotor and frontal areas that prevent unwanted reflexive saccades, such as
DLPFC (Guitton et al., 1985).
Although our cortical thickness analyses showed trends of decreased GM in the parietal
lobes, it seems improbable that this lesion was substantial enough to merit functional
compensation in PEF during preparation; while ALS patients did show a compensation-like
increase in BOLD signal for antisaccades, activation during prosaccade preparation closely
resembled that of control subjects. Because lesions to PEF have behavioural consequences such
as longer prosaccade SRTs (Pierrot-Deseilligny et al., 1991a), and our ALS subjects showed no
impairment to prosaccade latencies, it seems more plausible that increased activation during
antisaccade preparation in PEF is related to ALS patients compensating for the increased task
difficulty, rather than compensation due to structural damage.
We therefore investigated correlations between GM volumes and BOLD signal responses to
determine whether a compensatory mechanism was at work (Fig. 2.13). Because BOLD signal is
93
mostly measured from GM, reduced cortical thickness may explain why we saw functional
differences between ALS and controls. We found significant correlations within DLPFC, where
increased antisaccade preparatory activation correlated with increased lateral prefrontal GM in
control subjects; meanwhile, ALS subjects appeared to be approaching a negative correlation. It
is possible that this positive correlation within control subjects signifies a lack of compensation
required, since reduced GM volumes (perhaps as a result of atrophy associated with normal
aging) were not associated with increased functional compensation. Even though the ALS
patients did not show a significant negative correlation, they may still be compensating slightly
in comparison to controls. We also found significant negative correlations for pro- and
antisaccade execution where reduced GM volumes in areas that house oculomotor ROIs were
associated with increased activation in those same ROIs within the ALS group only. This appears
to represent functional compensation due to reduced GM in ALS patients compared to controls,
in lateral prefrontal and parietal regions.
Functional compensation has been shown to progressively adapt with the ongoing loss of
function in ALS (Lulé et al., 2007); therefore larger differences in brain activation between ALS
and control subjects as a result of compensation may become apparent with more severe
disease progression. Disease progression is also associated with greater spread of atrophy in
cortical and subcortical regions (Nair et al., 2010; Thivard et al., 2007), which would also
promote compensation to make up for a lack of structural integrity. Regardless, it is difficult to
say conclusively with the current data whether structural changes caused greater task difficulty
and subsequent functional compensation, or whether task difficulty compensation is
independent of structure.
94
2.4.6 Limitations and Future Directions
There are several limitations associated with conducting multidisciplinary research of this
nature within a fragile clinical population such as ALS. The most challenging physical restriction,
which led to a number of recruited patients being unable to complete the entire experiment,
was the impaired respiratory capacity that accompanies ALS. Patients who had a FVC of 60%
predicted or lower were unable to complete the fMRI portion of the experiment as a safety
precaution. One patient had a sufficient FVC, but had experienced sialorrhoea and had difficulty
breathing when lying down in the MRI, so the experiment was aborted. Other physical
restrictions that prevented us from doing the MRI session included failed safety screening (i.e.,
metal implants), and claustrophobia. Along a similar vein, further physical limitations were
experienced as a result of disease progression; more severe progression is usually associated
with worsened FVC, therefore we were unable to study brain function or structure in highly
advanced patients. Because of this, we were unable to gain insight on whether and/or how
cognitive decline worsens with progression, and how this influences brain pathology.
Although our research provides interesting insight into executive dysfunction in ALS, it
would be beneficial to recruit more ALS and control subjects to complete the entire protocol in
order to have 25 – 30 participants in each group. This should allow us to improve statistical
power in our measures. With the current sample size, we saw large variability in the behavioural
(Fig. 2.3) and functional data (Fig. 2.7, 2.9) that may have resulted from the continuum of
cognitive profiles within this ALS group. Future analyses to separate patients who are cognitively
impaired from those who are not (or less impaired) on measures of behaviour and brain
function (in terms of BOLD signal) might reveal less variability within these sub-groupings. If our
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preliminary results are strengthened by adding new data, and we are able to confidently identify
the neural correlates of cognitive decline in ALS, we may have a model system to use for critical
evaluation of future therapies and interventions that will be developed for ALS.
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Chapter 3
General Discussion
This research was conducted as the first to investigate cognitive impairment in ALS using:
neuropsychological assessments to evaluate cognitive and behavioural deviances; saccadic eye
movement tasks to assess changes in behavioural control; functional imaging to characterize
how underlying neural correlates of saccade control are altered in ALS; and structural imaging to
determine whether brain atrophy displayed frontotemporal and oculomotor involvement. We
predicted that ALS patients would be impaired on tests of executive function, including the
antisaccade task. We also proposed that impaired behavioural performance would correspond
to changes in functional activation during antisaccade preparation and cortical atrophy in frontal
lobe and oculomotor regions.
We provided evidence that ALS patients displayed cognitive impairments, and that the
antisaccade task may prove as a reliable tool for detecting such impairments in ALS (Fig. 2.3E).
ALS patients were biased towards express saccades and, consequently, had slightly increased
antisaccade error rates, both which may have been precipitated from high levels of activation
related to motor preparation in the superior colliculus (Figs. 2.3C, D) (Dorris et al., 1997; Dorris
& Munoz, 1998; Everling et al., 1998; Everling et al., 1999). Our functional imaging data revealed
trends that ALS patients had increased preparatory activation in oculomotor regions for
antisaccades, perhaps to functionally compensate for the added task difficulty and their
impaired executive control. Decreased grey matter in ALS patients revealed a pattern of
frontotemporal pathology, and overlapped with saccades control areas. We speculate that the
97
loss of cortical resources in frontal and temporal regions in ALS may underlie the resulting
functional changes (Abrahams et al., 2003), such as compensation, to counteract the executive
dysfunction and added task difficulty of voluntary saccades for ALS patients. However, it appears
as though this compensation is perhaps insufficient, since voluntary saccades were somewhat
slower and resulted in slightly more errors in the ALS group compared to controls.
3.1 Clinical Relevance
Cognitive impairment in ALS is still a relatively new facet of the disease, and despite its high
prevalence (Ringholz et al., 2005), it is not an official symptom for diagnosis. However, cognitive
decline in ALS has been widely documented in the literature, and in some cases can be detected
before any motor symptoms of ALS (Guedj et al., 2007; Mackenzie et al., 2005; Murphy et al.,
2007a). Therefore a patient’s cognitive status might be an important diagnostic or screening tool
for those at risk of developing the disease (i.e., family history). Cognitive tests alone, however,
may not provide the most reliable diagnosis, and therefore should be combined with other tools
such as neuroimaging (Barson et al., 2000; Strong et al., 1999), which are often inaccessible and
costly. Saccade tasks may provide an appropriate tool to complement neuropsychological tests
in identifying changes to cognition and behaviour. Based on our findings, changes in saccade
performance in ALS may correspond to specific functional and structural changes, which could
possibly provide an alternative supplementary diagnostic tool to neuroimaging. We not only
found that antisaccade errors are closely associated with executive dysfunction in ALS, but also
that antisaccade reaction times were related to functional changes that corresponded to a
particular pattern of brain atrophy (i.e. DLPFC and PEF).
98
Our findings have provided promising preliminary data, which, if expanded upon to create a
larger and more reliable data set, might provide a sensitive and accessible way of quantifying
neurological changes that are characteristic of ALS pathology. Saccades may also be an
appropriate tool for assessing cognitive function in highly progressed patients who have become
‘locked in’ due to loss of motor function. Even in severely progressed ALS patients, extraocular
muscles are somewhat resistant to motor neuron loss (Okamoto et al., 1993). This preservation
would allow us to quantify changes in saccade behaviour, and subsequently infer changes to
brain function and structure in severely progressed ALS patients who are unable to lie supine in
an MRI due to respiratory difficulties. Studying patients who vary in disease severity and
progression might allow us to map out a trajectory of ‘expected saccadic impairments as a result
of progression’, which could prove to be a valuable prognostic tool when assessing a patient’s
life expectancy.
This study provides important measures of brain structure and function in ALS that are
distinct from impairments seen in other neurodegenerative disorders. For example, patients
with Parkinson’s disease (PD) also have a neurodegenerative pathology that affects their
cognition and motor skills. ALS patients in our study and PD patients from the study of Cameron
and colleagues (2011) displayed similar saccade impairments such as increased difficulty with
the antisaccade task, and a bias towards automatic responses. However, ALS patients appeared
to make even more express saccades than PD patients, and the nature of functional changes in
PD were highly distinguishable from those seen in ALS. PD patients, both on and off
medications, had decreased preparatory activation in a number of ROIs during the antisaccade
task (Cameron et al., 2011), whereas ALS patients showed trends of increased preparatory
99
activation. This suggests that different pathological and compensatory processes are at work,
and that brain function and behaviour are influenced differently within these
neurodegenerative diseases. It is possible that upon further data collection of ALS saccade
behaviour, we could establish greater statistical power to unveil more subtle differences
between ALS and PD patients’ performance on saccade tasks.
3.2 Limitations
Additional limitations we experienced in the current study were predominantly of two
streams: 1) pragmatic limitations; and 2) data-related limitations. The major pragmatic
limitation encountered was the location in which this research took place. This research was
conducted in Kingston, Ontario, a city with a population of approximately 150,000 people, and
ALS patients were recruited from two neuromuscular clinics associated with Kingston hospitals.
The prevalence of ALS is said to be 6 per 100,000 people (Mitchell & Borasio, 2007). Since many
surrounding areas do not have ALS specialists, many patients from smaller municipalities
(excluding Toronto and Ottawa) travel to Kingston for specialized health care. Based on this
limited pool of subjects, we were only able to obtain consent from 21 eligible ALS patients for
this study. Since all ALS patients in this study were from Kingston or the surrounding areas, we
may have introduced an unavoidable sampling bias based on geographical location.
The limitations we experienced related to data were: 1) a non-random sample; 2) effects of
medications; 3) not modelling fixation, and 4) altered express saccade epoch. The participants
who completed our study were not randomly sampled; participation in this study depended
upon the patient: 1) living within the Kingston region; 2) giving informed consent; 3) not
meeting any exclusion criteria; and 4) successful data acquisition. Additionally, the sample was
100
heterogeneous in terms of symptom onset, disease duration and severity. Due to the small
cohort of patients we successfully ran, we did not have sufficient statistical power to categorize
patients based on their disease duration, onset, or severity of symptoms. As a result, we cannot
draw any conclusions about how the type of onset or disease severity influenced behaviour,
brain function, or structure. The patients we recruited were almost exclusively spinal-onset,
because most bulbar-onset patients experience early decline in their respiratory function,
making them poor candidates for completing the MRI protocol. Bulbar-onset, however, is also
associated with greater executive dysfunction than spinal-onset (Abrahams et al., 1997; David &
Gillhamm, 1986; Lomen-Hoerth et al., 2003; Massman et al., 1996), which perhaps would have
provided us with statistically significant findings when compared to control results.
Due to our limited access to ALS patients, we did not consider neurologically irrelevant
health afflictions as exclusion criteria. Patients who were included in the final analyses did not
display any current depression or anxiety symptoms (based on medical history and scoring on
the HADS), and had no other known neurological confounds. However, almost all patients were
taking medications (see Table 1), some which were for ALS symptom management/ treatment
such as Baclofen™ and Riluzole™, while others were for unrelated health conditions (i.e.,
diabetes, chronic obstructive pulmonary disease, high cholesterol, and gastric acid reflux).
Antianxiety and antidepressant medications were being taken by a number of patients, not due
to pre-existing clinical anxiety or depression disorders, but rather for managing the anxiety and
depression associated with having a terminal disease. We recognize that these medications have
powerful neurological effects which could potentially influence the oculomotor network;
however such investigations have not been explicitly studied in ALS. Eye movements have been
101
studied in patients with schizophrenia on and off antipsychotic medications, where
antipsychotics did not influence the rate of antisaccade errors, but did increase antisaccade SRTs
in non-medicated patients compared to medicated patients and controls (Hutton et al., 1998).
We must therefore interpret our results with caution. Some patients were also taking
antihypertensive medications, which may have influenced BOLD signal changes. While we
cannot exclude the possibility that these medications influenced our fMRI data, previous
findings support that beta-blocking antihypertensives did not affect cerebral blood flow,
cerebrovascular reactivity, or cognitive performance (Heinke, Zysset, HundGeorgiadis, Olthoff, &
von Cramon, 2005).
One saccade parameter that was not specifically looked at in this study, and which may have
caused a limitation in the interpretation of our data, was fixation. Evidence of saccadic
intrusions during fixation have been shown in ALS patients, where intrusions were greater in
amplitude (Donaghy et al., 2009) and frequency (Shaunak et al., 1995) for ALS patients
compared to controls. However, it was reported that the size of the intrusions were not
significantly different across ALS and controls when a fixation cue was present (Donaghy et al.,
2009). With the exception of the 200 ms “gap” in our paradigm, there was always a cue present
when subjects were asked to fixate; therefore this should not be of concern for our data.
Provided that we did not explicitly look at fixation in the current study (and fixation was not
modelled in our fMRI analyses), we cannot confirm that fixation abnormalities were absent in
this ALS subject pool. If abnormalities were present in the patient group, the baseline for all
fMRI analyses might be inconsistent across ALS and control groups. Future studies should
perhaps consider modelling fixation into the general linear model, so that brain activation
102
caused by saccadic intrusions during fixation can be isolated and removed from the contrasts,
rather than being concealed within the baseline measure of fixation.
When the behavioural parameters were chosen for this study, the express saccade epoch
was based on what had been documented in the human and monkey literature: reported
express saccade latencies tend to fall within 90 – 140 ms (Dorris et al., 1997; Fischer & Boch,
1983; Fischer & Weber, 1993; Munoz et al., 1998). Interestingly, however, we have observed
from previously conducted research in the Munoz lab (unpublished data) that saccadic reaction
times are often elongated when subjects are lying supine in an MRI compared to when they are
sitting upright performing saccade tasks. Prosaccade reaction times follow a bimodal
distribution, where express saccades are the first peak, and regular latency saccades are the
second peak. Based on the data we collected from ALS patients lying supine, their express
saccade epoch appeared to span from 90 – 160 ms, and therefore our epoch was adjusted
slightly from that reported in the human and monkey literature where subjects were sitting
upright. We therefore should exercise caution when making comparisons to other studies (such
as those looking at the frequency of express saccades in PD patients) where a different express
saccade epoch was used (i.e., (Cameron et al., 2011)).
3.3 Future Directions
In future research, it would be extremely valuable to do longitudinal large-scale studies to
determine how ALS patients’ cognitive abilities deteriorate over time and how this influences
their saccadic performance and corresponding functional and structural integrity. Evaluations of
cognitive performance have been shown to decline over time in bulbar-onset ALS patients
(Strong et al., 1999), so it is plausible that antisaccade performance (which correlates with
103
cognitive abilities) would also change with time. Based on the time constraints of a Master’s
program, I was unable to conduct longitudinal research within this cohort of patients.
Additionally, some of the patients within the sample progressed so rapidly after the first data
collection session that a follow-up session would not have been possible due to respiratory and
physical decline. However, I did have the opportunity to follow-up with one patient who
repeated the protocol 9 months after his initial participation in the study. Unfortunately,
findings from this one subject were inconclusive; only very subtle differences were seen across
neuropsychological, behavioural, functional and structural measures. It is therefore difficult to
conclude anything from this one follow-up since this patient might be particularly slow at
progressing. Perhaps if a third follow-up was conducted a number of months later, or if I had
been able to do follow-ups with more patients from this sample, I would have found more
substantial differences. This would be an extremely valuable arena to explore, as it could
provide important insight on the influence of disease progression on the ALS brain.
3.4 Summary and Conclusions
The results of our study demonstrate that, even with a small sample size, cognitive
impairments on tests of executive function can be detected in ALS patients. These deficits were
associated with more direction errors made on the antisaccade task, which is designed to probe
executive control. Surprisingly, ALS patients were biased towards rapid automatic responses
that were likely caused by increased preparatory activation in SC as a result of inadequate
suppression from higher-order cortical areas (Dorris et al., 1997; Guitton et al., 1985; Munoz &
Everling, 2004). We found evidence of functional compensation in ALS patients, which might
reflect greater task difficulty (perhaps as a result of cognitive impairment) or may signify
104
compensation for a lack of grey matter. To our knowledge, this study marks the first to combine
psychometric evaluations, saccade tasks, functional and structural imaging techniques to study
neurological changes in ALS. Our preliminary data are promising, and further data collection
may bring the trends we observed closer to statistical significance, allowing us to confidently
identify the neural correlates of cognitive decline in ALS. If, however, we fail to identify a neural
substrate for cognitive impairment in ALS with a larger data set, we will have to explore other
cognitive tasks that probe executive functions that lie beyond the oculomotor system.
105
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Appendix A
El Escorial World Federation of Neurology Revised Criteria for ALS Diagnosis. (Brooks, 1994;
Brooks et al., 2000).
The diagnosis of ALS requires the presence of:
1) evidence of lower motor neuron (LMN) degeneration by clinical,
electrophysiological or neuropathologic examination,
2) evidence of upper motor neuron (UMN) degeneration by clinical examination,
and
3) progressive spread of signs within a region or to other regions, as determined by
history or examination
together with the absence of:
1) electrophysiological or pathological evidence of other disease processes that
might explain the signs of LMN and/or UMN degeneration, and
2) neuroimaging evidence of other disease processes that might explain the
observed clinical and electrophysiological signs
“Clinically Definite ALS is defined on clinical evidence alone by the presence of UMN, as well as
LMN signs, in the bulbar region and at least two spinal regions or the presence of UMN and LMN
signs in three spinal regions.
Clinically Probable ALS is defined on clinical evidence alone by UMN and LMN signs in at least
two regions with some UMN signs necessarily rostral to (above) the LMN signs.
Clinically Probable ALS – Laboratory-supported is defined when clinical signs of UMN and LMN
dysfunction are in only one region, or when UMN signs alone are present in one region, and
LMN signs de.ned by EMG criteria are present in at least two regions, with proper application of
neuroimaging and clinical laboratory protocols to exclude other causes.
Clinically Possible ALS is defined when clinical signs of UMN and LMN dysfunction are found
together in only one region or UMN signs are found alone in two or more regions; or LMN signs
are found rostral to UMN signs and the diagnosis of Clinically Probable ALS – Laboratory
supported cannot be proven by evidence on clinical grounds in conjunction with
electrodiagnostic, neurophysiologic, neuroimaging or clinical laboratory studies. Other
diagnoses must have been excluded to accept a diagnosis of Clinically Possible ALS.
Clinically Suspected ALS may be suspected in many settings, where the diagnosis of ALS could
not be regarded as sufficiently certain to include the patient in a research study. Hence, this
category is deleted from the revised El Escorial Criteria for the Diagnosis of ALS.”
139