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MRI Based Quantification Of Cerebrovascular Health In Pediatric Subjects With Sickle Cell Disease
by
Junseok Kim
A thesis submitted in conformity with the requirements for the degree of Master’s of Science
Institute of Medical Science University of Toronto
© Copyright by Junseok Kim 2015
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MRI Based Quantification Of Cerebrovascular Health In The
Pediatric subjects With Sickle Cell Disease
Junseok Kim
Master’s of Science
Institute of Medical Science
University of Toronto
2015
Abstract
Sickle cell disease is a lifelong genetic disorder of the erythrocytes characterized by the sickling
of deoxygenated hemoglobin. As a result of the sickling, children with SCD suffer from many
different complications such as poor cerebrovascular health, vasculopathies and stroke. Using
MR, we can quantify the degree of cerebrovascular health impairment with cerebrovascular
reactivity. It was seen that children with SCD had significantly reduced CVR both regionally and
globally which was then seen to be correlated with regional measures of cortical thickness. From
this study, it was observed that regional deficits in cerebrovascular health are related to regional
cortical thinning. Furthermore, previous studies have demonstrated impaired vasculature in
patients with obstructive sleep apnea (OSA) which occurs at a high rate in SCD. Therefore, CVR
was measured in SCD patients with OSA and compared against SCD patients with no-OSA to
determine the effect of OSA in children with SCD.
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Acknowledgments
I would like to first acknowledge my supervisor Dr. Andrea Kassner for her guidance and
support throughout my project. I would like to also thank the members of my committee Drs.
David Mikulis and Jason Lerch who have provided insightful discussion and ideas throughout
the project. I would also like to thank Dr. Indra Narang who has allowed me to be actively
involved in the field of sleep which has been an interesting addition to my project. I would also
like to thank the current and past members of my lab Jackie, David, Paula, Fred who have been a
great help to me on a day to day basis throughout my two and a half years at the lab. I would like
the thank the MRI techs Tammy, Ruth, Gary, Annette and Vivian for being so helpful during our
scans, I would also like to thank Dr. Odame, Dr. Kirby and the rest of the hematology staff
members for their generous help with recruitment and finally I would like to thank Dr. Shroff for
reviewing our images.
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TABLE OF CONTENTS
Acknowledgments .......................................................................................................................... iii
TABLE OF CONTENTS ..................................................................................................................... iv
List of Abbreviations .................................................................................................................... vii
List of Tables ................................................................................................................................. xi
List of Figures ............................................................................................................................... xii
1 Motivation and Outline .............................................................................................................. 1
1.1 Motivation ........................................................................................................................... 1
1.2 Outline ................................................................................................................................. 2
2 Sickle Cell Disease (SCD) ......................................................................................................... 4
2.1 Pathophysiology of SCD ..................................................................................................... 4
2.2 Epidemiology of SCD ......................................................................................................... 7
2.3 Treatment of SCD ............................................................................................................... 8
2.4 Effect on cerebrovascular health of SCD .......................................................................... 12
2.5 Cognitive deficits in SCD ................................................................................................. 13
2.6 Brain abnormalities in SCD .............................................................................................. 15
2.7 Obstructive Sleep Apnea (OSA) in SCD .......................................................................... 17
2.7.1 Pathophysiology of OSA ...................................................................................... 17
2.7.2 Diagnosis of OSA ................................................................................................. 19
2.7.3 Epidemiology of OSA ........................................................................................... 20
2.7.4 Treatment of OSA ................................................................................................. 20
2.7.5 Effect on cerebrovascular health of OSA ............................................................. 22
2.7.6 Cognitive deficits in OSA ..................................................................................... 23
3 Magnetic resonance Imaging ................................................................................................... 25
3.1 Introduction to MRI .......................................................................................................... 25
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3.2 Fundamentals of MRI ....................................................................................................... 25
3.3 Applications of MRI in the Brain ..................................................................................... 27
3.4 Imaging cerebrovascular disease ...................................................................................... 28
3.5 MRI based cerebrovascular reactivity (CVR) ................................................................... 31
3.6 Mechanism of CVR .......................................................................................................... 33
3.7 Post processing of MRI data ............................................................................................. 34
4 Hypothesis ................................................................................................................................ 36
5 CVR and cortical thickness in SCD ......................................................................................... 37
5.1 Introduction ....................................................................................................................... 37
5.2 Methods ............................................................................................................................. 38
5.2.1 Subject recruitment ............................................................................................... 38
5.2.2 CO2 breathing challenge ....................................................................................... 39
5.2.3 Magnetic resonance imaging ................................................................................ 41
5.2.4 CVR Data processing ............................................................................................ 41
5.2.5 Cortical thickness and surface area data processing ............................................. 42
5.2.6 Statistical analysis ................................................................................................. 43
5.3 Results ............................................................................................................................... 43
5.3.1 Subject recruitment ............................................................................................... 43
5.3.2 CVR in the SCD group compared to controls ...................................................... 44
5.3.3 Cortical thickness in the SCD group compared to controls .................................. 51
5.3.4 Association of CVR and cortical thickness in the SCD group compared to
controls .................................................................................................................. 59
5.4 Discussion ......................................................................................................................... 61
5.5 Conclusion ........................................................................................................................ 65
6 SCD and effect of OSA on CVR .............................................................................................. 66
6.1 Introduction ....................................................................................................................... 66
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6.2 Methods ............................................................................................................................. 67
6.2.1 Subject recruitment ............................................................................................... 67
6.2.2 Polysomnography (PSG) ...................................................................................... 68
6.2.3 Inducing end-tidal CO2 changes ........................................................................... 68
6.2.4 Magnetic resonance imaging ................................................................................ 69
6.2.5 CVR data processing ............................................................................................. 69
6.2.6 Statistical analysis ................................................................................................. 69
6.3 Results ............................................................................................................................... 69
6.3.1 Patient recruitment ................................................................................................ 69
6.3.2 Global CVR comparisons between the OSA group and the no OSA group ......... 70
6.3.3 Regional CVR comparisons between the OSA group and the no OSA group ..... 71
6.3.4 Global association between CVR and PSG measures .......................................... 73
6.3.5 Regional association between CVR and PSG measures ....................................... 75
6.4 Discussion ......................................................................................................................... 81
6.5 Conclusion ........................................................................................................................ 83
7 Discussion and conclusion ....................................................................................................... 84
7.1 Overall discussion ............................................................................................................. 84
7.2 Limitations ........................................................................................................................ 86
7.3 Future Directions .............................................................................................................. 88
7.4 Conclusion ........................................................................................................................ 92
8 References ................................................................................................................................ 94
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List of Abbreviations
SCD sickle cell disease
RBC red blood cell
HbSS sickle cell anemia
HbSB thalassemia
HU hydroxyurea
NO nitric oxide
Tx transfusion
HbF hemoglobin F
TCD transcranial Doppler
ACS acute chest syndrome
IQ intelligence quotient
ADHD attention deficit hyperactivity disorder
FLAIR fluid attenuated inversion recovery
MRA magnetic resonance angiography
OSA obstructive sleep apnea
AHI apnea-hypopnea index
OAHI Obstructive Apnea-Hypopnea Index
BMI body mass index
PSG polysomnography
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CPAP continuous positive airway pressure
SaO2 oxygen saturation
REM random eye movement
O2 oxygen
CO2 carbon dioxide
CVR Cerebrovascular reactivity
MRI Magnetic resonance imaging
BOLD blood level oxygen level dependent
ROS reactive oxygen species
H+
proton
RF radiofrequency
PD proton density
ASL arterial spin labeling
fMRI functional magnetic resonance imaging
SNR signal to noise ratio
TOF time of flight
CBF cerebral blood flow
MPET model-driven prospective end-tidal
PETCO2 prospective end tidal CO2
PETO2 prospective end tidal O2
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FSL FMRIB Software Library
GM grey matter
WM white matter
CSF cerebral spine fluid
CLASP Constrained Laplacian Anatomical Segmentation using Proximities
ANOVA Analysis of variance
ROI region of interest
AVG average
Stdev standard deviation
CT cortical thickness
SA surface area
DMN default mode network
DTI diffusion tensor imaging
ADC apparent diffusion constant
MD mean diffusivity
FA fractional anisotropy
T1 spin-lattice relaxation time
T2 spin-spin relaxation time
TE echo time
TR repetition time
x
CMRO2 oxygen metabolism
OEF oxygen extraction fraction
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List of Tables
Table 1 Cognitive deficits in SCD adapted from (Hijmans et al. 2011) ....................................... 14
Table 2 Subject demographics ...................................................................................................... 39
Table 3 Regional CVR comparisons between SCD and controls ................................................. 47
Table 4 Regional cortical thickness comparisons between SCD and controls ............................. 54
Table 5 Significant regional associations between CVR and cortical thickness .......................... 61
Table 6 Patient demographics ....................................................................................................... 68
Table 7 Regional CVR comparisons ............................................................................................. 71
Table 8 Regional correlation between nocturnal oxygenation and CVR ..................................... 77
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List of Figures
Figure 1 comparison between normal erythrocytes and sickled erythrocytes adapted from
(Frenette and Atweh 2007) ............................................................................................................. 4
Figure 2 Pathophysiology of SCD adapted from (Rees et al. 2010) ............................................... 6
Figure 3 Mechanism of NO depletion adapted from (Kato et al. 2007) ........................................ 7
Figure 4 Distribution of HbS allele frequency around the world adapted from (Piel et al. 2010) . 8
Figure 5 Effect of HU treatment adapted from (Strouse et al. 2008) ........................................... 11
Figure 6 Schematic of OSA compared to healthy controls adapted from
(https://myhealth.alberta.ca/health/pages/conditions.aspx?hwid=hw49127) ............................... 18
Figure 7 CPAP treatment used to open the airways during sleep in OSA adapted from
(http://getsleepapneatreatment.com/wp-content/uploads/2012/09/cpap-therapy1.jpg) ................ 22
Figure 8 Risk of stroke determined by TCD (Adams et al. 1992) ................................................ 29
Figure 9 The BOLD response paradigm ....................................................................................... 32
Figure 10 M atching of the BOLD signal to the CO2 signal to produce the CVR map .............. 33
Figure 11 Gas challenge apparatus and paradigm ........................................................................ 40
Figure 12 CVR comparison between healthy and SCD patients ................................................. 44
Figure 13 group comparisons between controls (black) and SCD (white) for global CVR ......... 45
Figure 14 group comparisons between controls (black) and SCD (white) for A & B) regional
CVR. A) right precentral gyrus, left superior frontal gyrus, left inferior frontal gyrus, right insula
B) right anterior cingulate cortex, right inferior frontal gyrus, left superior parietal gyrus, right
temporal pole ................................................................................................................................ 46
xiii
Figure 15 group comparisons between controls (black) and SCD (white) for global cortical
thickness ........................................................................................................................................ 52
Figure 16 group comparisons between controls (black) and SCD (white) for regional cortical
thickness (right precentral gyrus, left superior frontal gyrus, left median cingulate gyrus, right
inferior occipital gyrus, right temporal pole) ................................................................................ 53
Figure 17 association analysis between CVR and cortical thickness for SCD patients, normalized
to control data; A) right temporal pole (AAL84), first order polynomial B) left cuneus (AAL45),
second degree polynomial ............................................................................................................. 60
Figure 18 Proposed sigmoid model of association between cortical thickness and CVR ............ 64
Figure 19 Comparison of global CVR between OSA (red) and no-OSA SCD patients ............... 70
Figure 20 Regional CVR comparison between OSA (red) and No-OSA SCD patients. AAL2
(Right Precentral gyrus), AAL3 (Left Superior frontal gyrus), AAL8 (Right Middle frontal
gyrus), AAL36 (Right Posterior cingulate gyrus), AAL48 (Right Lingual gyrus), AAL53 (Left
Inferior occipital gyrus) ................................................................................................................ 71
Figure 21 Global association between CVR and Total sleep time SaO2 in OSA patients ........... 74
Figure 22 Regional association between CVR and REM SaO2 in OSA patients for AAL 18
(rolandic operculum) and AAL62 (Right inferior parietal) .......................................................... 76
1
1 Motivation and Outline
1.1 Motivation
Sickle cell disease (SCD) is a lifelong genetic disorder of the erythrocytes characterized by the
sickling and increased adhesion of deoxygenated hemoglobin. As a result, the vessels become
injured which may lead to downstream cascades of inflammation and oxidative stress. When
these events occur periodically, children with SCD suffer from many different complications
such as severe pain episodes, vasculopathies, organ damage and cerebrovascular diseases. These
complications may severely reduce the quality of life in SCD patients. Stroke in particular is the
most devastating complication of SCD which can occur frequently due to the impairment of
cerebrovasculature of the brain which disturbs the normal hemodynamic process. As such, there
exists a need to gauge the state of cerebrovascular health in children with SCD, a measure which
can be extremely useful to determine the extent of hemodynamic compromise caused by the
various complications in SCD. Using magnetic resonance imaging (MRI), we can quantify the
degree of cerebrovascular health impairment in SCD with cerebrovascular reactivity (CVR).
CVR measures the vasodilatory capacity of the cerebral blood vessels in response to a vasoactive
stimulus thus CVR can act as a surrogate measure for cerebrovascular health. CVR can then be
compared between SCD patients and healthy controls to determine the differences in vascular
reserve.
Previous studies have also demonstrated cognitive deficits in SCD patients without any visible
damage on clinical MRI scans. Therefore, advanced imaging methods are necessary to determine
the possible cause of cognitive impairment in this population such as brain volume analysis and
cortical thickness analysis. Previous studies have investigated brain structural abnormalities in
SCD which has revealed white matter injury, reduced grey matter volume and reductions of grey
matter T1. One study demonstrated regionally reductions in cortical thickness in patients with
SCD. While these previous studies have demonstrated structural abnormalities in SCD, the cause
remained unknown. One potential cause could be the impaired cerebrovascular health in patients
with SCD. Several studies have reported reduced CVR in patients with SCD which could
potentially disrupt the brain structural integrity in SCD. Therefore, to investigate if the impaired
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cerebrovascular health is a possible cause for structural abnormalities in SCD, regional CVR was
associated with regional cortical thinning.
As CVR was observed to be reduced in SCD, it is important to maintain CVR in children with
SCD. As such other complications which may reduce CVR should be carefully managed such
as obstructive sleep apnea (OSA), a sleep related breathing disorder which causes intermittent
hypoxia and hypercapnia during sleep. It was observed that the incidence rate of obstructive
sleep apnea (OSA) was high in children with SCD and, an independent risk factor for
endothelial dysfunction, it was unknown if OSA could further deteriorate cerebrovascular
health in children with SCD. Thus CVR was measured and compared between SCD patients
with OSA and with No-OSA to observe the effects of OSA on the cerebrovasculature. This will
allow us to gauge the effect of OSA on the cerebrovasculature in children with SCD which may
lead to changes in patient treatment paradigms in the future.
1.2 Outline
This thesis investigates the effect of SCD on cerebrovascular health using MR based methods.
The experimental findings in chapter 4 and 5 are work carried out at the Hospital for Sick
Children between September 2012 and December 2014.
Chapter 1 is the motivation behind the thesis and the outline for the thesis.
Chapter 2 is the literature review section of the disease models which contains relevant
information on the two disease discussed in the thesis. The first disease discussed is sickle cell
disease and the background information includes pathophysiology, etiology, effect on
cerebrovasculature, brain abnormalities and neurocognition. The second disease is obstructive
sleep apnea and the background information includes pathophysiology, etiology, effect on
cerebrovasculature, brain abnormalities, neurocognition and association with sickle cell disease.
This chapter serves to describe the diseases which were investigated and provide context in
which why these diseases were chosen for the experimental studies.
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Chapter 3 contains background information on magnetic resonance imaging such as basic
fundamentals of the MRI and the applications of MRI. As well, there is a discussion on BOLD
cerebrovascular reactivity which details the reason behind using BOLD based CVR as well as
the mechanism behind the CVR measurement. This chapter provides context on why MRI was
utilized in the experimental chapters while also discussing the relevant technical details.
Chapter 4 is the hypotheses for the project
Chapter 5 presents findings from the cortical thickness and CVR association study. This study
linked reduced regional CVR and regional cortical thinning in SCD. It was observed that reduced
CVR was associated with regional reductions in cortical thickness especially located in regions
of high metabolic demand.
Chapter 6 presents findings from the effect of OSA in SCD study which quantified the effects of
OSA on the cerebrovasculature of patients with SCD. The results demonstrated that individuals
with concomitant OSA had reduced CVR compared to those without OSA.
Chapter 7 is the discussions chapter. The first part of the chapter is the general discussion section
which provides the interpretation of the data and the limitations found in the studies. The second
part of the chapter is the future directions section.
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2 Sickle Cell Disease (SCD)
2.1 Pathophysiology of SCD
Sickle cell disease is one of the most common genetic disorders which affects the erythrocytes of
1 in 400 African Americans (Hassell 2010). There are several forms of SCD. Sickle cell anemia
(SS) is the most common and severe form of the disease. Other forms include the less severe SC
trait or the SB trait (thalassemia). Genetic mutation in SCD is caused by a single base
substitution from thymine to adenine which changes the expressed amino acid from valine to
glutamic acid in the beta globin gene. This mutation leads to the formation of hydrophobic
motifs in the deoxygenated sickle hemoglobin tetramer which causes the beta 1 and beta 2 chains
to bind. As a result, the structural integrity of the deoxygenated hemoglobin becomes disrupted
and it leads to the formation of sickle shaped erythrocytes.
Figure 1 comparison between normal erythrocytes and sickled erythrocytes adapted from
(Frenette and Atweh 2007)
Sickle erythrocyte polymerization leads to several pathophysiological processes. Among these,
vaso-occlusion and hemolytic anemia are the two main pathophysiological processes in SCD
(Bunn 1997). Vaso-occlusion results from blockage of the vessels caused by increased adhesion
of the sickled erythrocytes to the vessel wall in addition to increased cell-cell adhesion of sickled
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erythrocytes. This process leads to ischemic damage of the organs and is mainly responsible for
complications such as pain crises and ischemic organ damage (Ballas and Marcolina 2006). Pain
crisis is the most common complication in SCD and it is also the leading cause of hospital visits
in SCD (Platt et al. 1991). When there is tissue ischemia, SCD patients suffer episodes of intense
pain which is normally treated with strong pain relief medication at the hospital. Other
complication such as chronic organ damage also occur throughout life as frequent episodes of
vascular occlusions lead to periods of ischemia causing hypoxic damage. When the vasculature
is occluded, inflammation and oxidative damage occur. Inflammation and oxidative damage
impairs endothelial function which further worsens vaso-occlusive events. Organs which are
most vulnerable to vaso-occlusive infarction include the spleen, kidney and lungs which all may
fail to function during a patient’s life span.
6
Figure 2 Pathophysiology of SCD adapted from (Rees et al. 2010)
The other common pathophysiological process in SCD is hemolytic anemia, defined as failure to
deliver adequate oxygen throughout the body due to hemolysis. In addition to increased adhesion,
sickled erythrocytes have significantly shorter life span compared to healthy erythrocytes and
several mechanisms are responsible for the shorter lifespan of sickled hemoglobin.
Polymerization of HbS lead to deformation of the erythrocyte membrane and these cells are
prematurely destroyed as a result (Reiter et al. 2002). Furthermore, phagocytic destruction of
sickled erythrocytes is a common occurrence in SCD. This may be caused by vaso-occlusive
activation of phagocytes or hemoglobin-haptoglobin binding which induces phagocytic induction
of these molecules (Kristiansen et al. 2001). As a result, increased rate of hemolysis leads to
anemia. In addition, hemolysis can lead to adverse changes of hemodynamics within the body
which can impact the disease processes in SCD. The main factor which links hemolysis and
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abnormal hemodynamics is nitric oxide (NO). NO is a molecule produced in the endothelial cells
of the body by NO synthase and it is a molecule which is closely regulated due to its role in
endothelial function. In SCD, the hemolytic process causes NO to bind to the destroyed
hemoglobin reducing the bioavailability of intravascular NO (Reiter et al. 2002). Furthermore,
NO prevents vaso-occlusive events by inhibiting the aggregation of platelets and preventing the
transcription of cell adhesion molecules (Gladwin and Kato 2005). In addition to reduced NO
bioavailability, destroyed hemoglobin also releases arginase to the vasculature which destroys L-
arginine, the substrate of NO synthesis (Morris et al. 2005). Therefore, not only is there less NO
available, there is less NO being produced due to hemolysis (Kato et al. 2007). As a result, there
is dysregulation of endothelial function which can lead to severe complications in SCDa when
combined with vaso-occlusive events.
Figure 3 Mechanism of NO depletion adapted from (Kato et al. 2007)
2.2 Epidemiology of SCD
It is believed that currently, as high as 100000 people in the US and approximately 300000
infants world-wide are afflicted with sickle cell anemia (WHO report 2010). This equates to
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approximately 1 in 500 African Americans having SCD and 1 in 12 having the SCD allele.
Individuals of Sub-Saharan African descent are at the highest risk for SCD however, other
ethnicities such as South American, Cuban, Central American, Saudi Arabian, Indian, and
Mediterranean countries such as Turkey, Greece, and Italy also suffer from SCD albeit at lower
rates compared to the African ethnicities. Majority of SCD patients have the HbSS form (~70%
of all cases) of the gene while others have HbSB or HbSC which are milder forms of the disease.
According to the Centers for Disease Control and prevention, SCD is responsible for 75000
annual cases of hospitalization making it a serious health concern. The mortality rate of SCD has
been decreased significantly in the past 20 years especially in children as health care and
treatment methods have improved in the US. It was observed that the mortality rate of SCD has
decreased 67% in SCD patients between the ages of 1-4, decreased by 35% between ages of 5-9,
decreased by 33% between ages of 10-14 and 22% between the ages of 15-19 (Hamideh and
Alvarez 2013) since the death rate has been reduced from 1.6/100000 to 0.6/100000.
Figure 4 Distribution of HbS allele frequency around the world adapted from (Piel et al.
2010)
2.3 Treatment of SCD
The cure for SCD is bone marrow grafting which leads to the production of healthy erythrocytes.
However, due to the difficulties in finding a matching donor for the bone marrow graft as well as
9
the hospital care costs involved with the procedure, bone marrow transplant is not a common
procedure in SCD. As a result only 15% of patients with SCD receive bone marrow transplants
(Dallas et al. 2013). Thus majority of patients rely on other treatment options. Currently, there
are two common treatment methods used in the clinics; transfusion therapy (Tx) and
hydroxyurea (HU).
Transfusion therapy is given to patients who are at highest risk for stroke. Stroke risk is
determined by transcranial Doppler (TCD) sonography, which measures the blood flow velocity
within large vessels in the circle of Willis (Adams et al. 1998). Patients with a flow velocity over
200 cm/s are classified as having the highest risk for future stroke and are prescribed transfusion
therapy for stroke prevention. When patients are assigned to transfusion therapy, the relative risk
of stroke was shown to be reduced by 90% compared to standard of care (Wang and Dwan 2013).
While transfusion is mainly provided as a preventative therapy to those who are at high risk for
stroke (Adams and Brambilla 2005), other studies have shown the effectiveness of transfusion
therapy in treating acute chest syndrome (Charache et al. 1995) and increasing the oxygen
carrying capacity in the body. However, studies have also shown that transfusion is not a good
treatment for vaso-occlusive crises (Charache et al. 1995) and the inherent side effects of
transfusion therapy such as alluminization, iron overload and hemolytic transfusion reaction, are
significant detriments preventing transfusion to be established as a standard therapeutic
procedure. Furthermore, a recent trial has demonstrated that chronic Tx does not prevent the
occurrence of silent infarcts in children with SCD (Hulbert et al. 2011). This study also reported
that regular Tx therapy did not always prevent the occurrence of overt stroke if the patients
hematocrit levels were severely reduced.
The alternative treatment method is HU therapy. HU is the only US Food and Drug
Administration approved drug in adults with SCD and it is believed to have many beneficial
effects. HU is believed to increase HbF levels (Charache et al. 1996; Lebensburger et al. 2010),
reduce leukocyte levels and cell-adhesion molecule levels to prevent vaso-occlusion (Benkerrou
2002; Platt 2008) and it also decreases the rate of hemolysis which helps to ameliorate
endothelial dysfunction from the free hemoglobin (Kato et al. 2007). As a result, individuals on
HU have reduced occurrence of vaso-occlusive pain crises, acute chest syndrome (ACS) and
10
reduced need for transfusions (Zimmerman et al. 2007). Furthermore, HU has led to the
reduction of hospital stay for children with SCD thereby reducing the cost of care (Wang et al.
2013). In addition to the benefits, relatively low cost and ease of administration of HU make it a
much more attractive option compared to Tx which is costly and more difficult for hospitals to
manage especially in third world countries. However, HU also has disadvantages such as large
number of non-responders and it is also a cytotoxic drug thus it needs to be administered in
controlled doses (Strouse et al. 2008). Furthermore, the long term effects of HU have not been
studied; there may be potential long-term carcinogenic or leukaemeiogenic effects (Charache et
al. 1995). Recently, the efficacy of HU treatment compared to Tx was studied during the Stroke
With Transfusions Changing to Hydroxyurea (SWiTCH) trial (Ware and Helms 2012). The
results from the SWiTCH trial demonstrated that Tx is superior to HU in stroke prevention.
However, levels of HbF were increased and levels of HbS were decreased in the HU group
compared to the Tx group.
Aside from Tx and HU, other options are under development for clinical use in SCD. Anti-
inflammatory and anti-adhesion molecules used in animal trials have demonstrated promising
results (Orringer et al. 2001; Matsui et al. 2002) for ameliorating SCD symptoms. However, the
method of administration for the drugs remains an issue. Furthermore, the long-term effects of
the drugs have not been tested yet. NO has also been scrutinized as a potential therapeutic
avenue. As NO bioavailability is limited in SCD due to hemolysis, the addition of NO may
potentially reduce the severity of vaso-occlusive events in SCD. Finally, gene therapy trials have
been successful in mice (Pawliuk et al. 2001; Imren et al. 2002; Levasseur et al. 2003); however,
gene therapy is currently not a realistic treatment option in humans due to difficulties in its
implementation.
11
Figure 5 Effect of HU treatment adapted from (Strouse et al. 2008)
12
2.4 Effect on cerebrovascular health of SCD
SCD patients often suffer from vasculopathies and other vessel related diseases due to
downstream effects of vaso-occlusion and hemolysis. With continued reperfusion damage to the
endothelia combined with NO driven endothelial dysfunction, the endothelia gradually fail to
function normally which amplifies vaso-occlusive complications. One of the most serious
complications resulting from vaso-occlusive events is ischemic damage. When the body is
unable to deliver adequate oxygen, tissues suffer infarction leading to pulmonary hypertension,
leg ulcers, priapism and most importantly stroke. Stroke is the most devastating complication in
SCD and it is believed that 11% of patients under the age of twenty suffer from ischemic stroke
(Adams 2007; Verduzco and Nathan 2009). Compared to the prevalence rate of 2 ~ 13 per
100000 in healthy children (Lynch et al. 2002), the risk of stroke in SCD is high and is the most
prevalent vascular complication in the pediatric population. With 50% of children demonstrating
motor, cognitive and speech deficits post-stroke (deVeber et al. 2000) childhood ischemic stroke
is treated as a life-long disorder. Stroke in SCD is believed to be caused by vaso-occlusive crises
and the effect of vaso-occlusion is magnified by prolonged vessel damage and endothelial
dysfunction. Furthermore, children have impaired ability to vasodilate due to impaired NO
pathway (Reiter et al. 2002; Wood and Granger 2007; Wood et al. 2008; Akinsheye and Klings
2010). All of these factors are able to explain why ischemic stroke is especially common in
children with SCD.
In addition to ischemic stroke, patients with SCD often suffer from strokes which do not have
overt clinical symptoms, known as silent infarcts. In literature, it was observed that up to 40% of
SCD patients were identified with silent infarcts (Bernaudin et al. 2014). The presence of silent
infarcts increased the risk of consequent ischemic stroke in SCD (Miller et al. 2001). While
many silent infarct studies were performed on older children, results from studies looking at
younger children demonstrated the occurrence of silent infarcts even in children who were
younger than the age of 6 (Kwiatkowski et al. 2009). Thus even though the risk of stroke is
highest before the age of 4, older pediatric patients are also at a higher risk of consequent stroke
due to the high incidence of silent infarcts. Furthermore, the presence of silent infarcts has been
associated with impaired cognitive ability (Schatz et al. 2001). Thus not only do silent infarcts
13
increase the risk for future stroke, they have immediate consequences. While silent infarcts are
common in SCD, the reasons for their occurrence are currently unknown. However, there are
known risk factors associated with silent infarcts including pain crises, seizures and leukocytosis
(Kinney et al. 1999). Thus, disease severity may play a role in the occurrence of silent infarcts in
SCD.
Several studies have also demonstrated reductions in CVR within SCD patients (Nur et al. 2009;
Prohovnik et al. 2009). Using TCD and computerized tomography (CT), vascular reactivity to a
vasoactive stimulus was quantified in patients with SCD and it was observed that SCD patients
had increased blood flow and reduced CVR compared to controls. These studies were able to
demonstrate reduced CVR in SCD compared to controls however, they had diverse subjects
pools in terms of age and severity of disease thus the effect of SCD on CVR was not fully clear.
Reduction in vasodilatory capacity may result from several factors in SCD. Increased rate of
hemolysis leads to reductions in the oxygen carrying capacity of blood. This results in increased
blood flow and reductions in vasodilatory capacity. Furthermore, there is increased oxygen
extraction from the remaining hemoglobin and increased deoxygenation of sickled hemoglobin
which increases the frequency of vaso-occlusive episodes. When vessels become occluded,
oxidative damage to the vessel wall occurs from reperfusion injury and impair vasodilation
(Hatzipantelis et al. 2013). Furthermore, complications such as stenosis and moyamoya can
impair vascular functioning. These processes can disrupt normal blood flow within the brain
which is already compromised in SCD due to anemia. The persistence of disrupted
hemodynamics in SCD increases the risk of cerebral infarction and it may also lead to other
complications which are not readily observed through clinical examination.
2.5 Cognitive deficits in SCD
As mentioned previously, episodes of overt stroke and silent infarcts may result in cognitive
deficits. However, studies have demonstrated that individuals without discernable lesions also
displayed cognitive deficits (Steen, Miles, et al. 2003; Steen, Fineberg-Buchner, et al. 2005)
which led to the conjecture that cognitive deficits in SCD are a common phenomenon and are
also a result of the disease process, not limited to observable physical insult to the brain. Past
studies have demonstrated that manifestation of specific cognitive deficits depend on the location
14
of the damage for both overt and silent infarcts (Kral et al. 2006). It was also observed that in
addition to white matter lesions, abnormalities in grey matter volume were associated with
reduced cognitive function (Steen, Miles, et al. 2003; Scantlebury et al. 2011). Therefore, the
combination of white matter and grey matter abnormalities may be a key factor in the wide
variety of cognitive deficits which are commonly observed in SCD. To elucidate the link
between cognitive deficits and brain structural abnormalities, several studies have been
performed using various imaging modalities. This was performed to detect changes in the brain
that were correlated with cognitive performance. Some of the cognitive functions which have
been highlighted in the past include poor school performance, attention deficits, visuo-motor
functioning, working memory and planning (Hijmans et al. 2011) which were obtained using
various psychological tests and surveys. Executive functions such as attention, planning and
working memory were all seen to be impaired in SCD and since ischemic injury to the frontal
cortex was a common occurrence in SCD there could be a link between the two.
Table 1 Cognitive deficits in SCD adapted from (Hijmans et al. 2011)
15
Early studies which correlated structural abnormalities with cognitive deficits utilized IQ
(intelligence quotient) as the main cognitive measure as IQ is highly correlated with school
performance. Wang et al., (2001) found that patients with silent infarcts had significantly lower
IQ compared to those without any neurological injury. Other papers (Watkins et al. 1998;
Thompson et al. 2003) revealed that individuals with overt stroke had lower IQ compared to
those with silent infarcts as well as those without any brain insults. However, Steen et al., (2005)
were able to demonstrate that individuals with normal MRI examinations and no vascular injury
still had lower IQ compared to healthy controls. This suggested that some patients may have
subclinical stroke which could not be diagnosed using the MRI due to limitations of the
technique (DeBaun et al. 1998). Other areas of cognitive functioning that were studied included
attention and executive function. Deficits in attention and executive function are closely linked to
school performance and they have been identified as an issue for a long time in the pediatric
SCD population. Severity of attention deficit in SCD has been described as being very similar to
deficits seen in attention deficit/hyperactivity disorder (ADHD) (Daly et al. 2012) as such the
severity of these deficits may adversely affect the daily living of the afflicted individuals
(Hijmans et al. 2009). The specific deficits that manifest are in sustained attention, inhibitory
control, working memory and aggressive behavior. Studies have suggested that as with general
intelligence, these deficits may be attributable to focal infarct damage in the brain, especially in
the frontal lobes (Brown et al. 2000; Schatz et al. 2001; Christ et al. 2007). However, as with
general intelligence, even those without clinical signs of infarct damage displayed attention
deficits (Kral et al. 2003). Thus other methods which can associate the attention and executive
function deficits with structural impairments may be necessary.
2.6 Brain abnormalities in SCD
Several studies in literature have demonstrated abnormal brain structure in patients with SCD
using several neuroimaging tools. One of the earliest studies on brain abnormalities in SCD
demonstrated T1 differences between SCD and controls in patients without abnormalities
observed on conventional clinical MRI examinations (Steen et al. 1998). This finding changed
the paradigm of the field in such a way that pathological processes were now thought to occur in
16
the brains of SCD patients without overt brain damage detected from magnetic resonance
angiography (MRA) or fluid attenuated inversion recovery (FLAIR) imaging. This study led to
numerous other studies which employed quantitative neuroimaging techniques to explore brains
structural differences between SCD patients and healthy controls. White matter abnormalities
became the focus for several papers when the link was made between white matter injury and
cognitive deficits in SCD (Watkins et al. 1998; Wang et al. 2001). Using voxel based
morphometry (VBM), white matter density, as well as grey matter density was compared
between SCD patients with white matter lesions, SCD patients without white matter lesions and
controls (Baldeweg et al. 2006). It was observed that SCD patients had reduced white matter and
grey matter densities compared to controls in the borderzone regions, furthermore, the patients
with lesions had significantly reduced white matter densities compared to those without lesions.
Recent studies have investigated the integrity of white matter tracts using diffusion tensor
imaging (DTI) in SCD (Scantlebury et al. 2011; Balci et al. 2012). These studies found that the
structural integrity of the white matter tracts was disrupted in the SCD population, as patients
with SCD demonstrated reduced fractional anisotropy and increased apparent diffusion
coefficient values in several brain regions. Furthermore, deficits in the white matter were
associated with cognitive function and deficits; it was suggested that structural abnormalities
may manifest as behavioural deficits (Scantlebury et al. 2011). While white matter injury has
been one of the focal points for research in the SCD population, changes in grey matter structure
have also been studied. The VBM study by Baldeweg et al. (2006) investigated grey matter
densities in SCD and observed reductions in grey matter volume. Other studies also investigated
volumetric differences using T1 images. Volumetric differences between SCD and controls were
reported to be different when considering age (Steen, Emudianughe, et al. 2005) and it was
suggested that these changes may arise due to developmental changes in SCD. When reduction
in brain volume was correlated with IQ, it was observed that larger reductions were correlated
with greater reductions in the IQ scores (Scantlebury et al. 2011). Thus grey matter and white
matter structural abnormalities which were not detected with clinical MRI were also found to
contribute to the cognitive deficits observed in this population. Most recently, cortical thickness
has been researched as it provides advanced imaging measures compared to brain volume, which
is a cruder measure that incorporates both cortical thickness and surface area (Dale et al. 1999;
Fischl et al. 1999; Fischl and Dale 2000). A study by Kirk et al. (2009) investigated regional
17
differences in cortical thickness between healthy controls and SCD children. It was observed that
SCD children had cortical thinning in several regions, especially in those of high metabolic
activity. As such, this study was able to demonstrate abnormal changes in the cortex in SCD
using advanced imaging methods however, this study could not determine why these changes
occur in children with SCD. Thus several studies demonstrated abnormal white matter and grey
matter in SCD however, no studies were able to investigate the possible reasons behind the
abnormalities. There is a knowledge gap in the literature which needed to be filled to determine
the possible causes of brain abnormalities in SCD which may lead to cognitive deficits.
2.7 Obstructive Sleep Apnea (OSA) in SCD
In literature, it has been observed that high percentage of patients with SCD suffer from
concomitant OSA (Kaleyias et al. 2008; Rosen et al. 2014). The high prevalence of OSA in the
SCD population is explained by the fact that due to their disease, patients with SCD tend to have
hyperplasia of their tonsils which hinders breathing especially in the supine position during sleep.
Furthermore, it is believed that systemic anemia in SCD lowers the overall oxygen saturation
during sleep thus even with mild sleep disordered breathing, SCD patients can experience
nocturnal hypoxia (Okoli et al. 2009), which can be diagnosed as OSA under normal
examination procedure. The presence of OSA in SCD can be detrimental if it is left untreated
since the adverse effects of the two diseases may display a synergizing effect. This would lead to
severe disruptions in normal hemodynamics, increased risk for stroke and other vascular diseases
and increased severity of cognitive deficits.
2.7.1 Pathophysiology of OSA
OSA is characterized by recurrent obstruction of the upper airway during sleep. With upper
airway blockage, there is constant disruption of airflow causing intermittent hypoxia,
hypercapnia and disruption of sleep. There is negative pressure build up in the upper airway
during inhalation which acts to force the airways to close but this is normally counteracted by the
18
surrounding pharyngeal dilator muscles which activate to keep the airways open. However in
OSA, the occurrence of airflow blockage during sleep occurs when these muscles fail to
sufficiently counteract the negative pressure for the airways to remain open. It is interesting that
even in severe OSA cases, apneic events only occur during sleep. This is believed to be
attributable to the loss of muscular reflexes in the sleep state. Muscle reflexes are crucial since
muscle activation occurs in response to the sensing of the collapsing airway caused by the
negative pressure during inhalation. As such, when the reflex is triggered, the muscles are
activated and it prevents the airway from collapsing. Furthermore, the muscles themselves react
in an attenuated fashion to negative pressure during sleep which also contributes to worsen the
situation. The prolonged over usage of the dilator muscles may also cause the muscles to
function improperly which could also lead to apnea.
Figure 6 Schematic of OSA compared to healthy controls adapted from
(https://myhealth.alberta.ca/health/pages/conditions.aspx?hwid=hw49127)
The result of the pathophysiological process in OSA is the clinical symptoms which are
commonly observed. As the airway becomes obstructed for prolonged periods, the oxygen levels
become reduced and the carbon dioxide levels increase. As a reactive measure, the body must
19
transition into an awake state to reverse the obstruction. The periodic arousals during sleep
results in sleep fragmentation and sleep deprivation. The adverse effect on sleep architecture is
believed to be directly linked to many of the cognitive deficits observed in OSA. It has also been
demonstrated that OSA increases the risk of systemic hypertension and vascular diseases, which
have been linked to the intermittent hypoxia and hypercapnia. The prolonged intermittent
hypoxia and hypercapnia have also been linked to endothelial dysfunction which is an
independent risk factor for various vascular diseases. As such, OSA significantly increases the
risk of vascular diseases as well as death.
2.7.2 Diagnosis of OSA
The severity of OSA determines the extent of sleep fragmentation, hypoxia and hypercapnia that
occur and it is determined using a scale known as apnea-hypopnea index (AHI). The AHI is a
quantitative measure which counts the total episodes of apnea and hypopnea per hour of sleep.
Apnea is defined as the blockage of air flow for a period of at least 10s and hypopnea is defined
abnormally shallow breathing for a period of at least 10s. An AHI value of 5 or greater is
believed to be the criteria for being diagnosed with OSA. The AHI is measured during a sleep
study known as polysomnography (PSG). PSG is an overnight sleep study where various
measures are taken before and during the onset of sleep. The measurements include
electroencephalography, electrooculography, and submental and bilateral anterior tibialis
electromyography. Respiratory measurements include chest wall and abdominal movements
recorded by chest and abdominal belts, nasal airflow using a nasal air pressure transducer and
nasal thermal sensor, oxygen saturation (SpO2), and transcutaneous carbon dioxide (CO2).
Information obtained from PSG include sleep onset latency, rapid eye movement (REM) latency
total sleep time, sleep efficiency, time spent in each sleep stage (N1-3 and REM), snoring and
body position. Respiratory data include counts and indices of obstructive apneas, obstructive
hypopneas, central apneas and mixed apneas. An obstructive apnea was scored when airflow
dropped by more than 90% from baseline for at least 90% of the entire respiratory event with
chest and/or abdominal motion throughout the entire event, for the duration of at least 2 baseline
breaths. An obstructive hypopnea was scored when airflow dropped at least 50% from baseline
20
for a duration of at least 2 baseline breaths, accompanied by a minimum 3% drop in SpO2,
arousal, or awakening. A central apnea was defined as cessation of airflow with the absence of
respiratory and abdominal effort for a minimum of 20 seconds or the duration of at least 2
baseline breaths, in which case the event must have been accompanied by a minimum 3% drop
in SpO2, arousal, or awakening. A mixed apnea was defined as a drop in airflow of more than
90% from baseline for at least 90% of the entire respiratory event, for a duration at a minimum
of 2 baseline breaths, which is associated with absent inspiratory effort in the initial portion of
the event, followed by resumption of inspiratory effort before the end of the event. OSA severity
was graded according to accepted clinical criteria.
2.7.3 Epidemiology of OSA
Since its recognition as a clinical issue, OSA has not been recognized as a serious disorder which
led to the high rates of under diagnosis. As such, the disease is undiscovered and untreated in
many patients in which it could be potentially harmful. From epidemiological studies, it was
observed that around 4 ~ 34% of children suffer from OSA (4% in the general population)
(Lumeng and Chervin 2008; Marcus et al. 2012). The reported prevalence rate could be much
higher due to the fact that as high as 90% of the individuals who may be suffering from OSA do
not realize that they may have OSA. It is interesting to note that in the obese population, the
reported prevalence of OSA is as high as 40% with many still being under diagnosed (Tauman
and Gozal 2006; Arens and Muzumdar 2010). As such, with obesity becoming a major epidemic
in society, OSA is becoming more and more prominent and thus there has been much more focus
on the effect of the disease.
2.7.4 Treatment of OSA
Currently there are two main treatments for OSA surgical treatment or continuous positive
airway pressure (CPAP). Surgical intervention is an oft-used treatment in OSA. In children,
enlarged adenoids or tonsils are the major cause of OSA and in these cases adenotonsilectomy is
21
performed to ameliorate the symptoms (Shintani et al. 1998). However, in cases where surgical
treatment is not feasible CPAP is utilized. CPAP has been established as the gold standard of
OSA treatment since its introduction in 1981 (Weaver et al. 2007). CPAP provides positive
pressure during sleep so that the airways remain forcibly open during sleep. However, the rate of
compliance for CPAP is ~75% in children and even lower in adults; as such even though CPAP
has been proven to be effective, it is not a perfect solution for OSA. Aside from the two main
treatments, other methods have also been developed to help with OSA in cases where
adenotonsilectomy and CPAP may not work. Intra-nasal drugs have been shown to improve AHI
measures and reduce the severity of OSA in children (Nixon and Brouillette 2002). The problem
is that it is currently unknown how long an individual needs to be on this treatment regime and if
this treatment is worthwhile considering the modest improvement of the disease process. For
mild to moderate OSA, oral appliances have also been used as a corrective device to reposition
the mouth (Li et al. 2013). Several studies observed that oral appliance treatment worked to
reduce AHI and other PSG measures (Barthlen et al. 2000; Lam et al. 2007; Hoekema et al.
2008). Finally, due to the fact that obesity is a large risk factor for OSA, weight loss has been
suggested as a method to combat OSA (Verhulst et al. 2007). Individuals who were on an intense
training regime improved greatly on their AHI scores and in addition, individuals who underwent
bariatric surgery showed vast improvements in AHI as well (Khan et al. 2013; Lukas et al. 2014).
While there are currently a number of treatments for OSA, many of the newer methods still
require double blind trials to prove their efficacy especially since there have been mixed levels of
effectiveness observed in the studies which investigated these treatments.
22
Figure 7 CPAP treatment used to open the airways during sleep in OSA adapted from
(http://getsleepapneatreatment.com/wp-content/uploads/2012/09/cpap-therapy1.jpg)
2.7.5 Effect on cerebrovascular health of OSA
As mentioned previously, OSA is associated with higher risk for vascular diseases such as
hypertension, myocardial infarction and stroke (Butt et al. 2010; Sánchez-de-la-Torre et al. 2013;
Baldi et al. 2014). There are several reasons why OSA contributes to the developing of vascular
diseases and they are all related to the intermittent blockage of the upper airways. On factor
which contributes to increased risk of vascular diseases is oxidative stress. During apnea, there is
intermittent hypoxia followed by the subsequent reperfusion. This increases the production of
23
reactive oxygen species (ROS) which can cause damage to nucleic acid, proteins and lipids.
Studies in OSA have shown an increase of ROS production in the OSA population which
supported the original idea (Lavie 2003; Gozal et al. 2007). The chronic hypoxia and
hypercapnia also leads to increased activation of the sympathetic system (Lam and Ip 2007),
which is most directly linked to hypertension but it is also associated with endothelial
dysfunction which independently increases the risk for cerebrovascular accidents. Endothelial
dysfunction refers to the offset of balance between vasodilation and vasoconstriction which can
lead to vessel wall damage and increased risk for atherosclerosis (Bonetti et al. 2003; Wierzbicki
et al. 2004). The development of endothelial dysfunction is closely related to the prolonged
sympathetic activation and oxidative damage. Thus the adverse effects of hypoxia may trigger a
cascade of unwanted complications.
Of all the complications, such as hypertension, cardiovascular diseases such as coronary heart
disease and arrhythmia, stroke is one of the most devastating complications. Stroke can be
caused by OSA and it can also lead to OSA (Hsieh et al. 2012; Wallace et al. 2012). After the
occurrence of stroke, damage to the upper airway control areas can lead to increased episodes of
OSA while people who suffer from OSA are 3 times more prone to ischemic stroke. Therefore,
one event can trigger the other in a series of positive feedback loops.
2.7.6 Cognitive deficits in OSA
Children afflicted with OSA suffer from a wide spectrum of cognitive deficits. Many of these are
believed to be related to disturbance of sleep but OSA is by far the leading cause for sleep
disturbances. Specific deficits include mood disturbance, behavioural problems and deficits in
attention, memory and executive function which were measured using surveys, tests and reports.
The main cognitive deficits which are reported are intelligence, attention and executive function
while memory, visuospatial function, language and sensorimotor function are much less reported
(Kohler et al. 2012). In these studies, general intelligence was seen to be significantly reduced in
the OSA population with the caveat that the control population had an above average IQ.
Parental reports have also demonstrated increased impulsivity, hyperactivity and aggression
24
which lead to increased frequency of detrimental conduct. Furthermore, association studies
between OSA and ADHD has revealed numerous similarities between the two diseases. Using
the diagnostic criteria for ADHD, it was noted that as high as 28% of individuals were identified
with symptoms of ADHD and OSA. Thus children with OSA are more likely to suffer from
problems with increased inattentiveness and hyperactivity which may also lead to social
problems. While externalizing behaviours such as aggressive behaviour and hyperactivity are
more common in OSA children, internalizing behaviour have also been cited as an issue as well.
Other functions such as language and visuospatial ability were also seen to be adversely effected
by OSA and while number of studies differ on whether or not there is a significant difference on
these domains, majority of studies do show abnormality in the OSA group.
25
3 Magnetic resonance Imaging
3.1 Introduction to MRI
Magnetic resonance imaging (MRI) is a popular imaging method which can produce images of
the body. The MRI scanner, as the name suggests, utilizes magnetic fields to manipulate protons
in water and fat of the body. The water molecules are the main targets of MR imaging since there
is an abundance of water in the body. By exciting water molecules and measuring the resulting
signal, the type of tissue based on the quantity of water molecules can be identified. These
properties of water and body tissue make MR a good clinical tool due to the high spatially
sensitivity achieved in MR imaging. In addition, the MRI is non-invasive due to the fact that
water molecules in the body are utilized as a contrast agent to distinguish between different
structures of the body. This is one of the main advantages that the MRI has over the other
established clinical imaging methods such as X-ray and positron emission tomography (PET)
which require exposure to ionizing radiation. Thus, the MRI is a safe and effective imaging
modality which can be utilized in many different applications.
3.2 Fundamentals of MRI
As mentioned previously, MRI utilizes protons or Hydrogen molecules (H+) in water, which
make up 70~80% of the body, as an endogenous contrast agent. When H+ molecules are placed
in a magnetic field, the behaviour of their magnetic field can be described as spins. These spins
have directional orientation which can be manipulated by applying pulse sequences. Through the
application of specialized pulse sequences to the H+, images of the body can be generated from
the measured signal. Pulse sequences consist of radiofrequency (RF) pulses and gradient pulses
designed to manipulate the magnetic field in the scanner. During rest state, the H+
in the body are
aligned to the main magnetic field produced by the MR magnet. The spins are then tipped when
the RF pulse is used to resonate the H+. As the spins tip away from the main magnetic field
orientation, the spins of H+
begin to immediately undergo a process of precession to return to the
orientation of the main magnetic field. Normally, all spins would be in resonance and all spins
would precess at the same rate when the RF pulse is applied which would not provide any spatial
26
specificity. However when gradient pulses are applied, the MR signal can be localized by
temporarily varying the strength of the magnetic field. Variation in the magnetic field leads to
linear variation of the resonance frequency (also known as Larmour frequency) along the x, y or
z direction in which the gradient field is applied. This can specify the location in which the RF
pulse will excite the H+. Coincidentally spins also precess at the Larmour frequency thus spatial
information from the precessing protons can be received by the receiver coil. This process of
excitation and precession is the fundamental in the detection of a MR signal from the H+.
MRI utilizes three principle properties of H+, namely proton density (PD), spin-lattice relaxation
time (T1) and spin-spin relaxation time (T2). Images weighted by PD distinguish anatomical
structures by the number of H+ in a given space. Tissues with more fluid will have more
hydrogen and emit a stronger signal compared to structures like bone. T1 and T2 relaxation times
represent different precession properties of H+ after excitation by a RF pulse in a certain media
and also affect the signal intensity in a given space. These constants characterize the exponential
decay of signal as the spins revert back to equilibrium state. The T1 is the time needed for 63%
of the H+
spins to revert back to the main field orientation after the RF pulse. The signal from the
spins is strongest immediately following the RF excitation, and gradually returns to zero in an
exponential manner as the spins return to equilibrium. During T1 decay, a second independent
form of exponential signal loss occurs known as T2 decay. The T2 effects arises from signal
incoherence due to out-of-phase spins. After the RF pulse, all spins begin in-phase but local field
variations and proton-proton interactions cause different precession rates for each spin, resulting
in a net decay of the signal. T2 decay is typically faster than T1. In addition to T2 decay, there is
a decay which occurs much more rapidly. This decay known as the T2* originates from the
inhomogeneity in the magnetic field which may result from susceptibility induced field
distortions by the tissue. Thus the T2* decay is useful for detecting inhomogeneous tissue such
as blood and it is commonly used to detect hemorrhages or calcifications. By exploiting the
properties of the H+ proton behavior in a magnetic field, a range of MRI pulse sequences have
been developed for clinical and research applications.
27
3.3 Applications of MRI in the Brain
Over the past 3 decades, MRI technology has evolved from basic anatomical imaging to
sophisticated sequences that probe the complex physiological processes of the human body. A
significant portion of MRI research has been focused on the brain as it is a difficult organ to
study in vivo. In general, there are two major types of MR imaging sequences for the brain:
structural and functional. Other types of MR imaging are also available, but they are less widely
used and beyond the scope of this thesis.
Structural imaging is the most common usage of MRI in the clinical setting and encompasses the
PD, T1, and T2 weighted mechanisms described earlier. PD weighted imaging is not common
for neuroimaging as it has been replaced by FLAIR sequences that have superior tissue to lesion
contrast. Instead, T1 and T2 weighting have been used extensively to identify regional
differences in anatomy and disease. T1 weighted images emphasize the differences in T1 decay
between tissues and suppress the T2 effects. This type of sequence is sensitive to identifying
different types of tissue and therefore well suited for distinguishing gray matter, white matter,
and cerebral spinal fluid in the brain. Conversely, T2 weighted images suppress the T1 effect and
signal contrast is dictated by T2 decay. The T1 and T2 images are useful for measuring cortical
thickness and brain volume changes (Fischl and Dale 2000; Good et al. 2001) and also have been
shown to be effective at identifying lesions and hyperintensities in the brain (Hajnal et al. 1996;
Barkhof and Scheltens 2002).
Functional MRI (fMRI) refers to imaging of the brain's response to a stimulus. This response can
take many different forms, but conventionally, it is detected through changes in cerebral blood
flow. Measuring blood flow, or perfusion, with MRI can be performed with arterial spin labeling
(ASL) sequence. With ASL, protons in the blood can be magnetically “tagged” and this can be
used to distinguish the tagged blood from the non-tagged blood. From this, we can track the
tagged blood move through the arteries in a given time frame and with some modeling, we can
obtain cerebral blood flow information (Barbier et al. 2001). While ASL does provide blood flow
information only from the capillaries, unlike other methods such as contrast imaging, poor signal
to noise ratio (SNR) prevent ASL from being used widely in clinical settings (Duyn et al. 2005).
Another technique that can be utilized to measure the relative blood flow is blood oxygen level
28
dependent (BOLD) imaging. BOLD utilizes the body’s hemoglobin as the contrast agent since
deoxy-hemoglobin is slightly paramagnetic compared to oxygenated hemoglobin and will
therefore cause local T2* signal loss (Ogawa et al. 1990). Regions of the brain with increased
perfusion will have a lower deoxy-hemoglobin concentration and consequently stronger signal.
Although BOLD imaging does not provide absolute quantification of perfusion like ASL, it is an
effective and widely available tool for measuring the relative changes in blood flow. The use of
BOLD MRI in conjunction with a stimulus is a well-established method for identifying regions
of brain activation during a particular task. This technique has allowed researchers to assign
different regions of the brain to specific function which has become the bases for numerous
projects.
3.4 Imaging cerebrovascular disease
Currently, there are several imaging modalities which are utilized for imaging abnormal brain
hemodynamics. One of the most common methods for detecting cerebrovascular abnormalities in
the brain is transcranial Doppler (TCD), which is a widely utilized in the clinical setting using
ultrasound, with the main advantages being that it is non-invasive and cost-effective (Wong et al.
2000). TCD is based on the assumption that blood flow velocity will increase if there is
abnormal blood flow without changes to the blood vessels. Thus, TCD can be utilized to detect
stenosis, vasospasms and shunting, which are used to determine the risk for consequent stroke
(de Bray et al. 1997; Kiliç et al. 1998; Ringelstein et al. 1998; Mascia et al. 2003). In SCD, TCD
has been an integral part of assessing stroke risk especially in children with SCD (Adams et al.
1992, 2004). Individuals with TCD velocities greater than 200 cm/s in the middle cerebral artery
has been identified as having a high risk for stroke in SCD thus TCD has been used widely in the
clinics to screen individuals at risk and transfer them to transfusion therapy if necessary (Adams
et al. 2004). For the clinical setting, TCD remains the standard protocol for assessing regional
cerebrovascular health in SCD however, poor spatial specificity and limited information
provided by the technique is inadequate for detailed analysis on the state of regional
cerebrovascular health. MRI is a modality which can address the disadvantages of TCD.
29
Figure 8 Risk of stroke determined by TCD (Adams et al. 1992)
30
With MRI, structural and physiological measures of the cerebrovasculature can be mapped.
Structural measures of the cerebrovasculature includes vessel imaging such as the time of flight
angiography (TOF) and fluid attenuated inversion recovery (FLAIR) imaging. TOF angiography
can detect the presence of stenosis (DeMarco et al. 2004; Anzalone et al. 2005), image the
presence of hemorrhage (Chu et al. 2004), and to determine if treatment is necessary for
occlusive aneurysms (Urbach et al. 2008). In SCD, TOF is mainly utilized to diagnose the
presence of stenosis and vascular occlusion. The resulting MRA from the TOF often display
occlusions or stenosis from the major arteries around the Circle of Willis which can be used for
diagnosis. The other commonly utilized MRI structural image is FLAIR and it is utilized for
diagnosis and treatment determination in several cerebrovascular diseases (Korogi et al. 1999;
Linfante et al. 1999; Schellinger et al. 1999; Sanossian et al. 2007). However, it is also used to
detect the presence of lesions in the brain (Brant-Zawadzki et al. 1996; Hajnal et al. 1996;
Barkhof and Scheltens 2002). In SCD, FLAIR has demonstrated the presence of white matter
lesions and hyperintesities (Hogan et al. 2006; Scantlebury et al. 2011) which are related to
vascular complications in SCD. In addition, the presence of white matter lesions increases the
risk of consequent stroke in SCD which supports previous data. With MRA and FLAIR, vascular
abnormalities are visually apparent, however it is difficult to obtain a quantitative measure with
these techniques. Thus physiological measures of cerebrovasculature such as cerebral blood flow
(CBF) can provide this measure to compliment structural abnormalities.
Measures of CBF can be obtained with MRI using arterial spin labeling (ASL) (Williams et al.
1992; Wong et al. 1997) which provides a measure for cerebral perfusion. Measures of CBF are
integral to measuring changes in cerebral perfusion in response to adverse changes to the
vasculature. This was evident when ASL measures of perfusion were utilized to identify acute
stroke patients who had reduced CBF (Chalela et al. 2000), which provided a non-invasive
method for measuring CBF that could be used to prevent ischemic damage in acute stroke
patients through drug administration. Furthermore measures of perfusion was also utilized to
determine the extent of lesions after stroke as reductions in CBF would indicate further lesion
growth (Fiehler et al. 2002). Thus in acute stroke, where CBF information could be critical, ASL
provides a non-invasive alternative to PET for measuring CBF. In SCD, ASL has not been
established as a standard clinical tool in favour of TCD however it has been utilized in numerous
31
studies to investigate abnormal hemodynamics. Several studies have demonstrated increased
CBF values in SCD patients compared to healthy controls (Oguz et al. 2003; Helton et al. 2009;
Van Den Tweel et al. 2009) which was expected as a result of severe anemia. Furthermore,
several studies have associated abnormalities in CBF with other disease pathologies in SCD and
found that there was an inverse relation with IQ, vasodilatory capacity, severity of anemia and
white blood cell count (Strouse et al. 2006; Nur et al. 2009; Prohovnik et al. 2009; Hijmans et al.
2011). As such, these studies demonstrated the potential of using quantitative measures of
impaired hemodynamics as an alternative tool to assess severity of SCD and investigate the
consequent effects of impaired hemodynamics.
3.5 MRI based cerebrovascular reactivity (CVR)
CVR is a measure of vasodilatory capacity and it has shown potential as a clinical tool as a
predictor for subsequent stroke (Han et al. 2011; Gupta et al. 2012; Zhou 2014). As such CVR is
a good technique for assessing impaired hemodynamics in a population which may be at a high
risk for stroke. Measures of CVR can be obtained using TCD, PET, CT or MRI. While other
neuroimaging techniques are feasible, MRI is favoured since it is non-invasive and regionally
specific. This allows for repeated measures of CVR as well as the ability to identify regions with
limited vasodilatory capacity. MRI techniques which have been utilized in the measurement of
CVR include BOLD and ASL. ASL has been utilized in previous studies for measuring CVR as
the sequence can provide absolute CBF information (Tancredi et al. 2012; Inoue et al. 2014) and
it has been established as a valid method for measuring CVR (Heijtel et al. 2014). However,
there are no scientific publications that have shown ASL utility in accurately measuring flow in
patients with advanced cerebrovascular disease. The reason for this is that the labeled protons
lose signal while in transit to vascular beds distal to high grade stenoses secondary to long transit
times. Thus ASL is less optimal compared to BOLD in measuring CVR. With stronger SNR,
relative ease of access and short scan times, BOLD based CVR has many features which make it
attractive for researchers and clinicians alike. Despite all the advantages, BOLD is limited by the
fact that the measured signal changes are not only dependent on changes in CBF but other factors
such as blood volume, hematocrit and PaO2. Despite the technical shortcomings, BOLD CVR has
32
been used widely in the past and it has been proven to be as reliable as other methods of CVR
measurement.
Figure 9 The BOLD response paradigm
The administration of the vasoactive stimulus has also been varied in several previous CVR
studies. Several studies utilized breath-hold techniques (Tchistiakova et al. 2014; Geranmayeh et
al. 2015), other studies utilized fixed CO2 inhalation (Thomas et al. 2013; Lu et al. 2014) and
some studies utilized vasoactive drugs (Noguchi et al. 2015; Siero et al. 2015). The breath-hold
method is the easiest to administer as no specialized equipment is required and no external
supply of CO2 is required. The limitation of the breath-hold technique is that it is difficult to
monitor how hypercapnic the subjects become. The limited respiratory data from the breath-hold
technique thus makes it difficult to produce quantitative CVR maps and therefore the method is
qualitative with reproducibility issues. The fixed CO2 method can address the short comings of
the breath-hold technique. With fixed CO2, end-tidal CO2 (PETCO2) can be measured, thus
providing respiratory data which can be used for quantitative mapping of CVR. However, the
changes in levels of CO2 may be slow and unsteady in the fixed inhalation paradigm and it does
not account for concurrent changes in the partial pressure of O2 during the administration of CO2.
These limitations may reduce the reproducibility of the method which hinders its usefulness in
research. Drug induced vasodilation methods also have several limitations such as the need to
administer an intravenous injection, slow time to vasodilation, variability in response of the
subject even with identical dosing, and drug side effects. The alternative method which is easily
administered, quantifiable and reproducible is the administration of a vasoactive stimulus using a
33
computer-controlled gas delivery system. With this system, targeted levels of PETCO2 can be
administered to each subject which is directly proportional to the subjects’ partial pressure of
arterial CO2 (PaCO2) (Young et al. 1991). Furthermore, rapid, controlled changes in PETCO2
levels could be induced using a computer-controlled system which is vastly superior to all the
other alternatives. Precise targeting of PETCO2 is accomplished using a feed-forward algorithm
that allows for close matching of PETCO2 to predefined values (Slessarev et al. 2007) while also
controlling for the PaO2 levels to reduce the confounds. The closely controlled administration of
the vasoactive stimulus combined with BOLD results in a highly reproducible method of CVR.
Figure 10 M atching of the BOLD signal to the CO2 signal to produce the CVR map
3.6 Mechanism of CVR
CVR is measured modifying PaCO2 thereby inducing changes in CBF. Physiologically,
modifications in PaCO2 levels lead to vasodilation/vasoconstriction due to vessel action at the
level of arterioles and precapillary sphincters (Atkinson et al. 1990). Increases in CO2 levels lead
34
to relaxation of the smooth muscle in the cerebrovasculature especially the small vessels. While
the mechanism behind the smooth muscle relaxation has not been fully understood, one possible
mechanism that has been proposed is through the activation of pH sensitive K+
channels. During
periods of hypercapnia, there is a reduction in the pH which may trigger the ATP-sensitive K+
channels and the voltage gated K+
channels to open (Faraci and Sobey 1996). Opening of the K+
channels cause K+
efflux and hyperpolarization of the endothelial cells. During this process, the
endothelial smooth muscles may also become hyperpolarized which is coupled with the closing
of Ca2+
channels and vascular relaxation. In addition to the ion channel induced pathway of
vasodilation, release of NO and prostaglandins due to changes in sheer stress also contribute to
increased CBF during hypercapnia. Through this mechanism, the released NO causes
vasodilation of the cerebral vessels leading to increased CBF. Thus changes in pH due to
increased PaCO2 alter the CBF relatively rapidly. Another factor which contributes to the
regulation of CBF during hypercapnia is blood pressure. During hypercapnia, dynamic cerebral
autoregulation modifies blood pressure to increase CBF. Despite the number of different
mechanisms underlying CBF change in response to hypercapnia, modulation of pH and
oxygenation remain as the main driving factors which lead to CBF changes in the
cerebrovasculature.
3.7 Post processing of MRI data
Post processing of MRI data can be performed on many different platforms depending on the
data. FMRIB Software Library (FSL) is a popular tool which is often utilized to process BOLD
data. Through FSL, BOLD data can be registered to either the subject’s individual anatomical
space or a common space. Additionally, the PETCO2 data and corresponding BOLD data can be
linearly regressed to an existing model to obtain values of CVR that can be translated into a
parametric CVR map. Other tasks such as brain extraction, tissue classification and error
correction can also be performed which greatly improves the quality of the data. Other tools
which can be utilized to process MRI data include ones such as analysis of functional
neuroimages (AFNI) or CIVET which is normally utilized to process structural data. Using these
tools, the structural data can be transformed to be registered into the stereotactic space.
35
Classification of tissue into GM/WM/CSF and the creation of GM/WM/CSF surfaces using
various algorithms is integral in obtaining cortical thickness, brain volume or other structural
information from the data. Additionally, the processed data can be parcellated into specific
atlases or regional classification depending on the aims of the research.
36
4 Hypothesis
In our study we have 3 main hypotheses
1. CVR will be regionally reduced in children with SCD
compared to healthy controls
2. Reductions in CVR will be regionally associated with
reductions in cortical thickness in children with SCD
3. The concomitant presence of OSA will reduce CVR in the SCD
group with OSA compared to those without OSA
37
5 CVR and cortical thickness in SCD
5.1 Introduction
As mentioned in the background section, reduced vascular reserve in patients with SCD is
associated with increased risk of cerebral injury such as silent infarcts or overt stroke (Nur et al.
2009; Prohovnik et al. 2009). Furthermore, as we have observed, the occurrence of cerebral
injury has been associated with complications such as cognitive deficits in SCD (Steen et al.
1998; Pegelow et al. 2001; Dowling et al. 2010). However, SCD patients also exhibited cognitive
deficit even without visible lesions on anatomical MRI scans (Steen, Fineberg-Buchner, et al.
2005). Hence, more advanced imaging and processing techniques are necessary to detect
possible group differences in brain structure. A study by Kirk et al. (2009) investigated cortical
thickness as a potential neuroimaging marker and was able to identify regions of cortical
thinning in SCD. Moreover, cortical thinning was most severe in regions of high metabolic
activity. While the cause of cortical thinning is not clear, there could be a possible link between
cortical thinning and reduced vascular reserve. This is due to the fact that the regions with the
most severe thinning coincided with regions of high metabolic activity; therefore when there is
reduced vascular reserve in these regions, the body may not be able to sufficiently meet the
metabolic demands of the regions under hypoxic conditions.
CVR, as mentioned previously, can be quantified using advanced MR imaging and it measures
the change in cerebral blood flow in response to a vasoreactive stimulus to assess the vascular
reserve of cerebral blood vessel. Previous studies have demonstrated a global reduction of CVR
in SCD patients compared to controls as a result of endothelial dysfunction and hyperemia (Nur
et al. 2009; Prohovnik et al. 2009). However, it is not known how CVR reductions vary between
regions and whether the reductions are related to cortical thinning. Different brain regions have
different metabolic demands (Karbowski 2007) and as such it is likely that CVR reductions will
vary regionally in SCD. Therefore, the effect of hyperemic anemia on vascular reserve may
differ in severity for each brain region and the measured reduction in CVR attributable to chronic
dilation should vary depending on the blood supply required to sufficiently meet the metabolic
demands of the region. Regional variations in CVR reduction could help to explain the regional
38
differences in the degree of cortical thinning. By associating regional values of CVR and cortical
thickness, we explored the link between regionally reduced vascular reserve and cortical thinning
in SCD. Thus, we were able to test our hypothesis that CVR and cortical thickness could be
reduced in SCD and the regional reduction in CVR could be associated with regional cortical
thinning.
5.2 Methods
5.2.1 Subject recruitment
Patients for this study were recruited between Dec. 2009 and Nov. 2014 after Research and
Ethics Board approval from the hematology clinic at the Hospital for Sick Children. Our
inclusion criteria consisted of patients with HbSS genotype and participants between the age of
12 ~ 18 to limit age-related effects (Lenroot et al. 2007; Shaw et al. 2008). Patients on HU
treatment and patients with white matter hyperintensities were included in the study. The
exclusion criteria for the study were no history of psychological disease, pregnancy, major
cerebrovascular and cardiovascular disease. Patients on Tx treatment were excluded from the
study and patients with major stenosis, moyamoya or stroke were excluded from the study.
Control subjects were recruited from the community and they were age and sex matched to the
SCD subjects. Ethnicity and socioeconomic status were not matched between the groups in the
study. A complete demographic of the subject groups is presented on Table 2 Subject
demographics. Participants were asked to refrain from consuming vasoactive substances such as
caffeine or alcohol on the day of imaging. Informed written consent was obtained from each
subject or their parent/guardian.
39
Table 2 Subject demographics
SCD Controls
Total number
(males)
60 (29) 30 (13)
Age 14.5±2.56 15.2±3.16
Hematocrit 0.281±0.09 0.350 ~ 0.5
Number on HU 16 N/A
5.2.2 CO2 breathing challenge
The CO2 breathing challenge was administered using a model-driven prospective end-tidal
(MPET) system (RespirActTM
; Thornhill Research Inc.; Toronto, Canada). This computer-
controlled system regulates the flow and composition of gases (CO2, O2 and N2) based on each
subject's physiological parameters and delivers the gas mixture via a rebreathing mask and
circuit. The continuous delivery of specific gas concentrations enables fast and accurate
simultaneous targeting of end-tidal PCO2 (PETCO2) and end-tidal PO2 (PETO2), which have been
shown to closely correlate to arterial blood gas levels (Ito et al. 2008). Additional details about
the MPET system is provided by Slessarev et al. (2007). In this study, we implemented a block
design respiratory challenge consisting of alternating 60 second periods of normocapnia
(PETCO2 = 40 mmHg) and 45 second periods of hypercapnia (PETCO2 = 45 mmHg).
Concurrently, normoxia was maintained (PETO2= 100 mmHg) throughout the gas breathing
challenge. The total sequence runtime was 8 minutes. Sample lines in the breathing mask fed
into the RespirActTM
to continuously monitor partial pressures of the subject's expired gas. End-
40
tidal values were recorded at the end of each expired breath to define and plot the measured
PETCO2 and PETO2 temporal waveforms.
Figure 11 Gas challenge apparatus and paradigm
41
5.2.3 Magnetic resonance imaging
All imaging data were acquired on a clinical 3.0T MRI system (MAGNETOM Tim Trio;
Siemens Medical Solutions; Erlangen, Germany) with a 32-channel head coil. The CVR protocol
consisted of an 8 minute blood oxygen level dependent (BOLD) acquisition utilizing a single-
shot T2*-weighted echo-planar imaging sequence (TR/TE = 2000/30 ms, FA = 70°, FOV = 22
cm, matrix = 64×64, slices = 25, thickness = 4.5 mm, volumes = 240), which was run in
synchrony with the previously described CO2 breathing challenge. High resolution T1-weighted
anatomical images (TR/TE = 2300/2.96ms, FOV = 256mm, voxel size = 1.0×1.0×1.0mm, FA =
9°, parallel acquisition technique = 2) were then collected under normocapnia for co-registration,
segmentation and cortical thickness analysis. An expert radiologist reviewed all of the images to
identify possible existence of radiological pathology from the structural images.
5.2.4 CVR Data processing
BOLD MRI and CVR data were transferred to an independent workstation for post-processing
and analysis. Using MATLAB, we temporally aligned and resampled each PETCO2 waveform to
their respective BOLD datasets based on cross-correlation with the mean whole-brain BOLD
signal. CVR maps were then generated using FSL (FMRIB Software Library; The University of
Oxford, UK). The BOLD dynamics were first corrected for motion, spatially smoothed to reduce
noise, and temporally filtered to remove low frequency artifacts. A linear regression (FSL-
FEAT) of the BOLD signal for each voxel was performed with respect to the resampled PETCO2
waveform. The resulting voxel correlations formed a CVR map, which we then normalized to the
temporal mean BOLD signal map to represent CVR in terms of % ΔMR signal / mmHg (CO2).
These maps were coregistered to the high resolution T1 weighted anatomical images. GM and
WM masks were generated from the T1-weighted images by first using a brain extraction
algorithm (FSL-BET) to remove non-brain regions followed by automated tissue segmentation
(FSL-FAST).
42
Using the CIVET pipeline, CVR data were converted into the surface maps based on the
transformations performed on the corresponding high resolution anatomical images. The CIVET
pipeline sampled the CVR volume data and mapped it out as 40962 vertices for each hemisphere
based on the high resolution anatomical images. The surface maps produced were divided
according to AAL areas (Tzourio-Mazoyer et al. 2002) and GM/WM which were defined using a
territorial mask (78 AAL areas). The global and regional CVR averages and standard deviations
were calculated based on the AAL territory masks for both left and right hemisphere using
MATLAB.
5.2.5 Cortical thickness and surface area data processing
Structural MRIs were preprocessed using a standard processing protocol (linear registration into
standardized space, RF inhomogeneity correction) within the CIVET 1.1.12 processing pipeline
as described in Ad-D’ab’bagh et al. (2006). The MR images were linearly registered into a
common stereotactic space and were corrected for non-uniformity artifacts (Collins et al., 1994;
Sled et al., 1998). The processed MR images were then segmented according to their
physiological classification (grey matter, white matter, cerebrospinal fluid) (Zijdenbos et al.
2002; Tohka et al. 2004). The Constrained Laplacian Anatomical Segmentation using
Proximities (CLASP) method (Kim et al. 2005) was applied to produce the surfaces of grey and
white matter. The white matter (WM) surfaces were expanded out to the grey matter
(GM)/cerebrospinal fluid surface boundary using a surface deformation algorithm (Kim et al.,
2005). This procedure permits close matching of grey and white matter boundaries and cortical
thickness can be calculated based on the distance between the surfaces. This procedure resulted
in 40962 vertices for each hemisphere. The cortical surfaces were non-linearly aligned to a
standardized surface template (Lyttelton et al., 2007). Cortical thickness data were smoothed
following surface curvature using a blurring kernel of 20 mm. This technique enhances the
identification of cortical thickness changes (Lerch and Evans 2005). The global and regional
cortical thickness averages and standard deviations were calculated based on the AAL
segmentation for both left and right hemisphere using MATLAB. Finally, from the CLASP
algorithm produced GM surface, global and regional surface area averages and standard
43
deviations were calculated based on the AAL segmentation for both left and right hemisphere
using MATLAB.
5.2.6 Statistical analysis
All statistical analyses were performed using SPSS v22. The mean control group CVR values
was compared against the mean SCD group CVR values using the Student's t-test (p < 0.05) to
investigate mean differences using the AAL areas. The regional GM and WM cortical thickness
means were compared between the control group and the SCD group for each AAL area (p <
0.05). The regional GM surface area was compared between the control group and the SCD
group for each AAL area (p < 0.05) as well. In all the t-tests, a post-hoc Bonferroni test was
applied to limit the number of false positive results. The correlational analysis between CVR and
cortical thickness was performed in each AAL region. Both linear and second order polynomial
functions were utilized to model the correlation. Furthermore, ANOVA was performed to
determine if the two models were different for each area. The correlational analysis between
CVR and surface area was performed in each AAL region but only the linear correlation was
performed.
5.3 Results
5.3.1 Subject recruitment
Imaging data were acquired from sixty SCD patients (29 males and 31 females) between 12 and
18 years old and 30 controls. Three sets of SCD data were discarded after analysis due to motion
artefacts.
44
5.3.2 CVR in the SCD group compared to controls
CVR in the SCD group was significantly reduced compared to the control group within the left
GM (0.14±0.05 %ΔMR/mmHgCO2 SCD; 0.27±0.04 %ΔMR/mmHgCO2 control, p < 0.0001),
right GM (0.136±0.0550 %ΔMR/mmHgCO2 SCD; 0.28±0.04 %ΔMR/mmHgCO2 control, p <
0.0001), left WM (0.08±0.04 %ΔMR/mmHgCO2 SCD; 0.16±0.03 %ΔMR/mmHgCO2 control, p
< 0.0001) and right WM (0.09±0.03 %ΔMR/mmHgCO2 SCD; 0.16±0.03 %ΔMR/mmHgCO2
control, p < 0.0001) (Figure 13). In the regional analysis, there was significantly reduced CVR in
the SCD group in 71 out of the 78 AAL areas we investigated (p < 0.05) (Figure 14A/B).
Figure 12 CVR comparison between healthy and SCD patients
45
Figure 13 group comparisons between controls (black) and SCD (white) for global CVR
46
Figure 14 group comparisons between controls (black) and SCD (white) for A & B)
regional CVR. A) right precentral gyrus, left superior frontal gyrus, left inferior frontal
gyrus, right insula B) right anterior cingulate cortex, right inferior frontal gyrus, left
superior parietal gyrus, right temporal pole
47
Table 3 Regional CVR comparisons between SCD and controls
Control avg
CVR (%ΔMR/mmHgCO2)
Control Stdev (%ΔMR/mmHgCO2)
SCD avg CVR (%ΔMR/mmHgCO2)
SCD Stdev (%ΔMR/mmHgCO2)
Corrected
t-test p value
PreCG.L 0.241606 0.067346 0.128132 0.061507 1.10E-13
PreCG.R 0.256902 0.079931 0.128967 0.068622 2.72E-12
SFGdor.L 0.258803 0.083777 0.132867 0.076411 1.43E-10
SFGdor.R 0.251184 0.096689 0.12913 0.076842 9.45E-09
SFGorb.L 0.207039 0.116737 0.077194 0.087291 0.00018196
SFGorb.R 0.171947 0.115344 0.079998 0.084847 0.034073874
MFG.L 0.248507 0.077859 0.134098 0.075139 2.25E-10
MFG.R 0.24269 0.082311 0.126687 0.069161 2.44E-10
MFGorb.L 0.270182 0.143472 0.120295 0.115152 0.000990872
MFGorb.R 0.252491 0.14973 0.12667 0.091492 0.009615715
IFGoperc.L 0.229022 0.069059 0.119478 0.060514 2.54E-10
IFGoperc.R 0.220766 0.072342 0.118792 0.060061 1.12E-08
IFGtraing.L 0.247916 0.076096 0.134898 0.063572 2.83E-10
IFGtriang.R 0.238424 0.06778 0.129431 0.059798 8.26E-11
IFGorb.L 0.292143 0.094276 0.140126 0.088108 1.21E-09
IFGorb.R 0.25865 0.110404 0.129484 0.092695 3.41E-06
ROL.L 0.223705 0.080192 0.117949 0.058158 6.41E-10
48
ROL.R 0.221898 0.091124 0.118366 0.06506 7.27E-07
SMA.L 0.243636 0.097533 0.14282 0.070723 3.13E-07
SMA.R 0.262031 0.103963 0.125963 0.071466 5.66E-10
OLF.L 0.232314 0.133222 0.110482 0.142702 Ns
OLF.R 0.19641 0.104937 0.099892 0.163682 Ns
SFGmed.L 0.246494 0.09982 0.147441 0.070579 7.60E-06
SFGmed.R 0.291062 0.110079 0.149909 0.083569 7.64E-09
SFGmedorb.L 0.24651 0.14674 0.131801 0.109979 Ns
SFGmedorb.R 0.23793 0.128127 0.117348 0.101271 0.003998846
REC.L 0.175019 0.111255 0.074347 0.104582 0.024806416
REC.R 0.151989 0.107541 0.067344 0.098579 Ns
INS.L 0.234876 0.072901 0.131121 0.055863 5.53E-11
INS.R 0.223294 0.075482 0.12596 0.054386 2.09E-07
ACG.L 0.21947 0.094496 0.128038 0.066172 5.20E-05
ACG.R 0.211685 0.071347 0.113011 0.054417 1.73E-10
DCG.L 0.243882 0.089921 0.138808 0.067604 3.44E-07
DCG.R 0.241925 0.089159 0.126945 0.060992 1.35E-08
PCG.L 0.366532 0.120165 0.246298 0.113005 3.38E-05
PCG.R 0.329381 0.120674 0.222205 0.099151 4.36E-05
49
PHG.L 0.18994 0.055277 0.131635 0.049555 2.53E-07
PHG.R 0.196257 0.05725 0.128983 0.048253 1.33E-08
CAL.L 0.362464 0.106583 0.198363 0.080895 3.26E-09
CAL.R 0.368133 0.128147 0.19815 0.092193 1.36E-07
CUN.L 0.278448 0.083756 0.157503 0.059804 6.18E-11
CUN.R 0.259329 0.077967 0.159906 0.062252 1.20E-08
LING.L 0.335745 0.114554 0.213693 0.076713 1.54E-05
LING.R 0.344921 0.119898 0.214632 0.073937 2.90E-06
SOG.L 0.196905 0.058735 0.115561 0.055576 2.80E-10
SOG.R 0.207501 0.073119 0.125652 0.062395 4.77E-08
MOG.L 0.217057 0.067898 0.136254 0.061708 1.78E-07
MOG.R 0.207088 0.055286 0.140163 0.06618 3.20E-07
IOG.L 0.355594 0.160927 0.225733 0.114111 0.014908902
IOG.R 0.383736 0.14595 0.217386 0.127973 5.95E-05
FFG.L 0.26881 0.107823 0.187843 0.070862 0.017941053
FFG.R 0.259006 0.103064 0.178065 0.065668 0.008703086
PoCG.L 0.247994 0.077505 0.124689 0.058744 1.25E-12
PoCG.R 0.234847 0.068084 0.117722 0.061424 1.08E-11
SPG.L 0.224555 0.071335 0.12404 0.057232 5.10E-10
50
SPG.R 0.236672 0.074715 0.129628 0.064066 1.57E-09
IPL.L 0.210278 0.062907 0.122739 0.056905 4.58E-11
IPL.R 0.213588 0.071652 0.12253 0.056939 2.36E-08
SMG.L 0.264704 0.094167 0.142835 0.063822 5.65E-10
SMG.R 0.256546 0.091089 0.138602 0.062062 6.93E-08
ANG.L 0.240102 0.0926 0.12799 0.057897 2.49E-09
ANG.R 0.249422 0.084935 0.131016 0.059054 4.23E-09
PCUN.L 0.305542 0.097778 0.171396 0.070878 5.25E-10
PCUN.R 0.280768 0.088954 0.168654 0.068342 1.17E-08
PCL.L 0.27391 0.11086 0.141324 0.081579 4.93E-09
PCL.R 0.274207 0.099741 0.145448 0.089067 8.25E-09
HES.L 0.279883 0.090742 0.146567 0.066165 1.36E-11
HES.R 0.263237 0.109868 0.139992 0.06116 1.61E-06
STG.L 0.294633 0.079174 0.169124 0.065006 3.61E-13
STG.R 0.279785 0.071507 0.151457 0.059567 3.93E-11
TPOsup.L 0.209788 0.097947 0.176316 0.090771 Ns
TPOsup.R 0.262689 0.104151 0.138154 0.07429 1.85E-05
MTG.L 0.253704 0.07182 0.143104 0.057878 6.99E-11
MTG.R 0.258187 0.071204 0.143075 0.058839 1.96E-11
51
TPOmid.L 0.174134 0.092319 0.159548 0.127423 Ns
TPOmid.R 0.175153 0.086158 0.13243 0.103506 Ns
ITG.L 0.231573 0.086509 0.14238 0.076833 0.000230701
ITG.R 0.26235 0.088131 0.151395 0.069427 1.11E-07
PreCG - precntral gyrus; SFG - superior frontal gyrus; dor – dorsolateral; orb – orbital; MFG – middle frontal gyrus; IFG – inferior frontal gyrus;
operc – opercular; triang – triangular; ROL – rolandic; SMA – supplementary motor area; OLF – olfactory cortex; REC – gyrus rectus; INS –
insula; ACG – anterior cingulate gyrus; DCG – median cingulate gyrus; PCG – posterior cingulate gyrus; PHG – parahippocampal gyrus; CAL –
calcarine fissure; CUN – cuneus; LING – lingual gyrus; SOG – superior occipital gyrus; MOG – middle occipital gyrus; IOG – inferior occipital
gyrus; FFG – fusiform gyrus; SPG – superior parietal gyrus; PoCG – post central gyrus; IPL – inferior parietal gyrus; SMG – supramarginal
gyrus; ANG – angular gyrus; PCUN – precuneus; PCL – paracentral lobule; HES – Heschl gyrus; STG – superior temporal gyrus; TPO –
temporal pole; MTG – middle temporal gyrus; ITG – inferior temporal gyrus; sup – superior; mid – middle
5.3.3 Cortical thickness in the SCD group compared to controls
Mean cortical thickness in the SCD group (3.29±0.34 mm) was significantly reduced compared
to controls (3.45±0.3 mm, p < 0.0001) (Figure 15). In the regional analysis, cortical thickness
was reduced in 60 out of the 78 AAL areas (p < 0.05) but only 37 out of 78 areas after multiple
comparisons (Figure 16).
52
Figure 15 group comparisons between controls (black) and SCD (white) for global cortical
thickness
53
Figure 16 group comparisons between controls (black) and SCD (white) for regional
cortical thickness (right precentral gyrus, left superior frontal gyrus, left median cingulate
gyrus, right inferior occipital gyrus, right temporal pole)
54
Table 4 Regional cortical thickness comparisons between SCD and controls
Control avg CT
(mm)
Control Stdev
(mm)
SCD avg CT
(mm)
SCD Stdev
(mm)
Corrected
t-test p value
PreCG.L 3.219858 0.146984 3.059394 0.195694 0.003783457
PreCG.R 3.14653 0.174362 2.993341 0.233224 Ns
SFGdor.L 3.365093 0.21782 3.165516 0.237822 0.014837302
SFGdor.R 3.322473 0.185273 3.176385 0.256138 Ns
SFGorb.L 3.32732 0.166475 3.254258 0.197529 Ns
SFGorb.R 3.354918 0.164239 3.242701 0.226813 Ns
MFG.L 3.388092 0.177711 3.216083 0.201882 0.008758217
MFG.R 3.313551 0.195713 3.204085 0.238041 Ns
MFGorb.L 3.425787 0.165925 3.241709 0.242071 0.00535643
MFGorb.R 3.364058 0.194036 3.231157 0.286166 Ns
IFGoperc.L 3.640311 0.198889 3.496995 0.201372 Ns
IFGoperc.R 3.659798 0.211961 3.518324 0.28042 Ns
IFGtraing.L 3.39484 0.174872 3.239257 0.174389 0.015434517
IFGtriang.R 3.410274 0.198365 3.231534 0.274215 0.049291138
IFGorb.L 3.658043 0.183931 3.492305 0.237736 0.036164986
IFGorb.R 3.734456 0.216478 3.565093 0.267434 Ns
ROL.L 3.726484 0.196831 3.487119 0.19175 0.000131998
55
ROL.R 3.735658 0.241297 3.536099 0.331336 Ns
SMA.L 3.791163 0.218161 3.532062 0.283115 0.000880928
SMA.R 3.664569 0.215862 3.457474 0.311856 0.031204698
OLF.L 3.736142 0.269927 3.698355 0.238946 Ns
OLF.R 3.872664 0.206789 3.797232 0.262478 Ns
SFGmed.L 3.702316 0.238913 3.472982 0.265483 0.008894157
SFGmed.R 3.550286 0.208349 3.339757 0.31165 0.020847244
SFGmedorb.L 3.602326 0.197832 3.485551 0.256401 Ns
SFGmedorb.R 3.591392 0.181371 3.520738 0.270279 Ns
REC.L 3.288045 0.172693 3.168302 0.200712 Ns
REC.R 3.310782 0.15976 3.229416 0.212237 Ns
INS.L 4.540538 0.212943 4.363711 0.272751 Ns
INS.R 4.722423 0.317947 4.41143 0.442064 0.020355274
ACG.L 3.893946 0.278438 3.641889 0.323642 0.021843709
ACG.R 3.775258 0.305027 3.530647 0.278842 0.038646468
DCG.L 3.681912 0.169728 3.533033 0.213313 0.042758095
DCG.R 3.744248 0.180674 3.594808 0.218608 Ns
PCG.L 3.968969 0.194486 3.890028 0.232761 Ns
PCG.R 3.949261 0.172216 3.897703 0.267694 Ns
56
PHG.L 3.623012 0.157907 3.475998 0.208028 0.02747312
PHG.R 3.726924 0.216563 3.536468 0.242169 0.026173163
CAL.L 2.948502 0.175771 2.768788 0.188581 0.003273317
CAL.R 3.048938 0.179939 2.961621 0.17911 Ns
CUN.L 3.070752 0.187699 2.888217 0.170183 0.00368208
CUN.R 3.114015 0.158146 2.976348 0.169691 0.025336156
LING.L 3.230026 0.173826 3.089334 0.170833 0.042421014
LING.R 3.316592 0.201726 3.192506 0.157604 Ns
SOG.L 2.821111 0.177225 2.721929 0.178602 Ns
SOG.R 2.80551 0.183569 2.726017 0.185795 Ns
MOG.L 3.087904 0.15234 3.007291 0.159376 Ns
MOG.R 3.163981 0.154597 3.100445 0.166381 Ns
IOG.L 3.200096 0.1862 3.101614 0.176462 Ns
IOG.R 3.153236 0.183725 3.020442 0.198726 Ns
FFG.L 3.615571 0.17692 3.482068 0.196389 Ns
FFG.R 3.621291 0.16045 3.573647 0.173277 Ns
PoCG.L 2.89569 0.176796 2.662951 0.185488 3.97E-05
PoCG.R 2.845674 0.159719 2.729252 0.255388 Ns
SPG.L 3.025054 0.218088 2.86398 0.201331 Ns
57
SPG.R 2.952721 0.179868 2.847361 0.207665 Ns
IPL.L 3.237137 0.186914 3.110972 0.171706 Ns
IPL.R 3.298994 0.200778 3.193945 0.222089 Ns
SMG.L 3.470194 0.24763 3.269109 0.197061 0.024931417
SMG.R 3.459555 0.197015 3.23363 0.294635 0.00396388
ANG.L 3.32488 0.236369 3.212413 0.190995 Ns
ANG.R 3.321709 0.210306 3.22682 0.261922 Ns
PCUN.L 3.405436 0.174914 3.25901 0.173936 0.030643825
PCUN.R 3.443734 0.141366 3.34501 0.174397 Ns
PCL.L 3.184563 0.237397 2.977204 0.243142 0.020857244
PCL.R 2.925073 0.230523 2.783489 0.251859 Ns
HES.L 3.473115 0.154855 3.291937 0.219499 0.00202015
HES.R 3.528521 0.186585 3.279738 0.281874 0.000375904
STG.L 3.44465 0.192987 3.278901 0.187248 0.021423443
STG.R 3.527225 0.145753 3.322429 0.226331 0.000183031
TPOsup.L 4.066445 0.237446 3.818967 0.334876 0.010038894
TPOsup.R 4.054012 0.269447 3.825354 0.316364 0.046214929
MTG.L 3.50032 0.229294 3.301244 0.187287 0.012213463
MTG.R 3.601263 0.159406 3.425487 0.215623 0.003604804
58
TPOmid.L 4.01223 0.348688 3.719478 0.3661 0.035093011
TPOmid.R 3.857048 0.315807 3.63657 0.364754 Ns
ITG.L 3.60452 0.204517 3.40467 0.201872 0.004502328
ITG.R 3.572699 0.204556 3.454037 0.23168 Ns
PreCG - precntral gyrus; SFG - superior frontal gyrus; dor – dorsolateral; orb – orbital; MFG – middle frontal gyrus; IFG – inferior frontal gyrus;
operc – opercular; triang – triangular; ROL – rolandic; SMA – supplementary motor area; OLF – olfactory cortex; REC – gyrus rectus; INS –
insula; ACG – anterior cingulate gyrus; DCG – median cingulate gyrus; PCG – posterior cingulate gyrus; PHG – parahippocampal gyrus; CAL –
calcarine fissure; CUN – cuneus; LING – lingual gyrus; SOG – superior occipital gyrus; MOG – middle occipital gyrus; IOG – inferior occipital
gyrus; FFG – fusiform gyrus; SPG – superior parietal gyrus; PoCG – post central gyrus; IPL – inferior parietal gyrus; SMG – supramarginal
gyrus; ANG – angular gyrus; PCUN – precuneus; PCL – paracentral lobule; HES – Heschl gyrus; STG – superior temporal gyrus; TPO –
temporal pole; MTG – middle temporal gyrus; ITG – inferior temporal gyrus; sup – superior; mid – middle
59
5.3.4 Association of CVR and cortical thickness in the SCD group
compared to controls
In the regional association analysis, CVR was significantly associated with cortical thickness in
41 AAL regions of the brain (r > 0.48; p < 0.05). Furthermore the relationship between CVR and
cortical thickness was modeled by a second degree polynomial in 13 AAL regions while the
other 28 AAL regions were modeled by a first degree polynomial. There were strong correlations
in the AAL45 (left cuneus, r = 0.603), AAL58 (right post central gyrus, r = 0.633), AAL67 (left
precuneus, r = 0.604), AAL84 (right temporal pole, r = 0.626) while AAL areas corresponding to
the cingulate cortex (AAL32 ~ 36) was seen to have moderately strong correlation (r > 0.53) (fig.
16). When the same association analysis was applied to control subjects, there was no association
between CVR and cortical thickness in any of the AAL regions.
60
Figure 17 association analysis between CVR and cortical thickness for SCD patients,
normalized to control data; A) right temporal pole (AAL84), first order polynomial B) left
cuneus (AAL45), second degree polynomial
61
Table 5 Significant regional associations between CVR and cortical thickness
AAL area Polynomial model r value AAL area Polynomial model r value
PreCG.R 1st degree 0.55794 IOG.R 1st degree 0.41641
SFGdor.L 1st degree 0.43209 PoCG.L 1st degree 0.50596
MFGorb.L 2nd degree 0.35412 PoCG.R 2nd degree 0.63301
IFGtriang.L 1st degree 0.4613 IPL.L 1st degree 0.38872
IFGtriang.R 1st degree 0.56577 SMG.L 1st degree 0.47297
IFGorb.L 2nd degree 0.48518 SMG.R 1st degree 0.34482
SMA.L 1st degree 0.48135 ANG.L 1st degree 0.5013
SMA.R 2nd degree 0.42367 PCUN.L 2nd degree 0.5949
SFGmed.L 1st degree 0.46573 PCUN.R 1st degree 0.52029
SFGorb.R 2nd degree 0.44989 PCL.L 2nd degree 0.5002
SFGmedorb.R 2nd degree 0.34742 PCL.R 1st degree 0.59473
ACG.L 1st degree 0.43566 HES.R 1st degree 0.42202
ACG.R 1st degree 0.47149 TPOsup.L 1st degree 0.47234
DCG.L 1st degree 0.52934 TPOsup.R 1st degree 0.6261
DCG.R 1st degree 0.5331 MTG.R 2nd degree 0.41497
PCG.L 1st degree 0.53759 TPOmid.L 2nd degree 0.46217
PHG.L 1st degree 0.47371 TPOmid.R 1st degree 0.48755
PHG.R 2nd degree 0.51478 ITG.L 1st degree 0.31686
CAL.R 1st degree 0.56267 ITG.R 2nd degree 0.33317
CUN.L 2nd degree 0.62418
LING.L 1st degree 0.47728
SOG.R 2nd degree 0.42048
PreCG - precntral gyrus; SFG - superior frontal gyrus; dor – dorsolateral; orb – orbital; MFG – middle frontal gyrus; IFG – inferior frontal gyrus;
triang – triangular; SMA – supplementary motor area; ACG – anterior cingulate gyrus; DCG – median cingulate gyrus; PCG – posterior cingulate
gyrus; PHG – parahippocampal gyrus; CAL – calcarine fissure; CUN – cuneus; LING – lingual gyrus; SOG – superior occipital gyrus; IOG –
inferior occipital gyrus; PoCG – post central gyrus; IPL – inferior parietal gyrus; SMG – supramarginal gyrus; ANG – angular gyrus; PCUN –
precuneus; PCL – paracentral lobule; HES – Heschl gyrus; TPO – temporal pole; MTG – middle temporal gyrus; ITG – inferior temporal gyrus;
sup – superior; mid – middle
5.4 Discussion
In this study, we have quantified for the first time, the regional association between cortical
thickness and CVR in the pediatric SCD population. Individuals affected with SCD had
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significantly reduced vascular reserve and significantly thinner cortices. These results were
consistent with previous findings in literature which separately reported reduced global CVR
(Nur et al. 2009; Prohovnik et al. 2009) and cortical thickness (Kirk et al. 2009) in patients with
SCD. Regionally, there was also a statistically significant reduction in CVR for almost all the
AAL areas in SCD patients compared to the controls. However, the same did not hold true for
cortical thickness as a number of regions were not observed to be statistically different in the
SCD population. This result was similar to that of Kirk et al. (2009) who only discovered 14
ROIs with reduced cortical thickness. Many of the regions in our study overlapped with the ROIs
found by Kirk et al. (2009) (left and superior frontal gyrus, left pre/post central gyrus, left
precuneus, right precuneus and right middle temporal gyrus), but we also saw several other
regions that were not previously reported to be different between the groups as Kirk investigated
limited regions of interest compared to our study.
Subsequent investigation into the relation between CVR and cortical thickness revealed that
there was a significant association between the two in forty-one brain regions, which
demonstrated a regional link between reduced vascular reserve and cortical thinning. The process
of cortical thinning can occur from neuronal atrophy or axonal atrophy (Salat et al. 2004;
Overvliet et al. 2013) but it is unclear how this is linked to impaired CVR as observed. One
possible mechanism may be that the insufficient supply of nutrients to the brain tissue during
childhood results in delayed development of the brain and thinner cortices (Steen, Emudianughe,
et al. 2005). The other possible mechanism is neuronal cell atrophy, which may occur when CVR
is impaired as neurons do not receive sufficient blood flow when required (Bennett et al. 1998;
Cechetti et al. 2012; Hébert et al. 2013). Interestingly, this mechanism of atrophy seems to
support our data where we discovered a regional discrepancy in the strength of association
between CVR and cortical thickness. The particular regions exhibiting strong associations were
the cingulate cortexes (anterior and posterior), occipital lobe and the precuneus, which all shared
several characteristics that could explain the variation in the strength of association found in the
study. One common characteristic across these regions is high metabolic activity (Raichle et al.
2001), making them more vulnerable to hypoxic situations with a reduction in cerebrovascular
reserve. Severe regional cortical thinning in the high metabolic areas could then be the
consequence of prolonged reduction in vascular reserve in these patients. Finally, some of the
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regions have been classified as the watershed or borderline infarct regions (Mangla et al. 2011).
These regions (such as the cingulate, occipital cortex, regions of the parietal cortex and some
frontal cortex regions) are located at the junction of two major perfusion areas which are most
vulnerable to infarction due to the diminishing supply of blood flow in the overlapped regions
during periods of hypoperfusion. As such, watershed region areas revealed a high correlation
between reduced ability to dilate and having cortical thinning due to the fact that blood would be
supplied primarily to the well vascularized territories before it reached the distal regions during
periods of need.
This difference in association strength between the brain regions led to the application of
different association models in the study. Some brain regions were found to be significantly
related when modeled with a first degree polynomial while others were found to be significantly
related when modeled with a second degree polynomial. The reason behind this was that when
there is high CVR in some brain regions there could be enough perfusion to meet the metabolic
demands of the region despite the fact that CVR remains reduced compared to control values in
SCD. Thus there would be little to no cortical thinning associated with reduced CVR. This
phenomenon was demonstrated in the control population where there was no association between
CVR and cortical thickness in any brain regions probably due to the aforementioned fact that
controls had significantly higher CVR which could accommodate for the brain’s metabolic
needs. In the other extreme, having a very low CVR also had little to no association on the
severity of cortical thinning therefore, this may show that changes in CVR are not solely
responsible for changes in cortical thickness. Finally, there were regions other than the extremes
where CVR was strongly associated with cortical thickness. These were the regions where the
degree of CVR reduction was observed to have the greatest association with the severity of
cortical thinning. As such, the whole brain relationship between CVR and cortical thickness was
hypothesized to be best modeled by a sigmoidal curve (Figure 18) where the two plateau sections
depict either high CVR or low CVR which would not be associated with cortical thickness. In
between the two plateau sections, there is a linear section which would show the strong
association between CVR and cortical thickness. The different models were thus utilized to
model different aspects of the sigmoid.
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Figure 18 Proposed sigmoid model of association between cortical thickness and CVR
Whilst there seems to be evidence showing that reduced CVR is regionally associated with
reduced cortical thickness, there are several limitations which must be addressed. Although
regions of strong association coincided with regions of high metabolic demand, we cannot be
certain that the cortical atrophy is driven from the failure to meet metabolic demands of these
regions as a consequence of impaired vasodilation. Even with reduced regional vasodilation, the
compensatory increase in cerebral blood flow may be sufficient to supply the brain regions.
Therefore, measures of regional oxygen metabolism must be acquired in conjunction with CVR
and cortical thickness measures to further elucidate the possible link between adverse change in
hemodynamics, brain structural integrity and metabolic activity. Furthermore, to investigate the
cause and effect of the relationship between changes in hemodynamics and brain structural
integrity, a longitudinal study is necessary to monitor the changes in CVR along with changes in
cortical thickness over time. That would allow us to determine if cortical thinning occurs as a
result of atrophy or due to developmental abnormalities. In addition, we would be able to
determine if the vasculature changes precede the structural changes or vise versa.
Future studies should also consider cognitive data in conjunction with cortical thickness and
CVR data. Of all the brain regions which were found to have a strong association between CVR
and cortical thickness, several of the regions were cited to be involved with cognitive
65
functioning. These regions included the cingulate cortex and the prefrontal cortex which are
involved with cognitive functioning such as sustained attention, behavioural regulation and
executive function (Bush et al. 2000; Miller and Cohen 2001; Ridderinkhof et al. 2007; St Onge
and Floresco 2010; Stuss 2011; Gasquoine 2013; Leech and Sharp 2014) observed to be
impaired in SCD (Hijmans et al. 2011). In addition, the anterior cingulate cortex is also believed
to function improperly within the default mode network (DMN) and other neural networks in
children with attention deficit disorders (Lawrence et al. 2003; Sun et al. 2012; Leech and Sharp
2014). Thus it is possible that the reduced cortical thickness may be involved in the disruption of
the intricate neural circuits which contributes to the attention deficits commonly observed in
SCD. Aside from the cingulate cortex and prefrontal cortex, other areas such as the inferior and
temporal pole which are both believed to be involved with executive functions (Olson et al.
2007; Macuga and Frey 2011; Albein-Urios et al. 2013) were also seen to demonstrate a high
level of association between CVR and cortical thickness. Thus if an association between
cognitive data, CVR and cortical thickness is observed it may be possible to prevent cognitive
deficits by ameliorating the possible causes before the symptoms appear and when combined
with longitudinal data, we will be able to utilize measures of CVR or cortical thickness to
determine if specific cognitive deficits would be likely to appear.
5.5 Conclusion
In this study, we demonstrated reduced regional CVR and cortical thickness in children with
SCD. Our results indicate that there is a regional association between CVR and cortical thickness
in certain brain regions. Regions of high metabolic activity were seen to have stronger
association compared to other regions and this suggested that these regions were more likely to
be affected by reduced cerebrovascular reserve. However, due to the lack of oxygen metabolism
data, we cannot be certain that moderately strong correlation observed in the high metabolic
brain regions were due to metabolic demand not being met. Thus future work is necessary to
determine the exact cause of cortical thinning in SCD and its progression over time.
66
6 SCD and effect of OSA on CVR
6.1 Introduction
Sickle cell disease (SCD) is a genetic disease of the red blood cells (RBC) affecting 1 in 500
African Americans in the United States. In SCD, deoxygenated RBC becomes rigid and sickled
shape, which leads to increased cell-cell adhesion, reduced lifespan and decreased oxygen
carrying capacity compared to healthy hemoglobin (Hare 2004). Increased RBC adhesion to the
vessel endothelium and to each other lead to vascular occlusion, which triggers chronic systemic
inflammation and oxidative stress leading to endothelial dysfunction (Conran et al. 2009;
Akinsheye and Klings 2010; Hatzipantelis et al. 2013). Endothelial dysfunction in combination
with systemic anemia in patients with SCD, may lead to the exhaustion of vasodilatory capacity,
thus exposing them to high risk of ischemic injury and stroke (Prohovnik et al. 2009). Therefore,
maintaining a good level of vascular reserve becomes critical for individuals with SCD. However
there are several complications associated with SCD which can adversely affect the
cerebrovasculature.
Sleep disordered breathing is one of the common complications in SCD that can significantly
impact cerebrovascular health. In particular, obstructive sleep apnea (OSA) is highly prevalent in
children with SCD, occurring in 30% -70% of children with SCD (Kaleyias et al. 2008; Rosen et
al. 2014) compared to 1-4% of otherwise healthy children. OSA is characterized by recurrent
obstruction of the upper airway during sleep that results in disruption of nocturnal ventilation,
causing intermittent hypoxia as well as sleep fragmentation. Furthermore, OSA and associated
intermittent nocturnal hypoxia is thought to contribute to SCD-associated morbidities including
vaso-occlusive disease and endothelial dysfunction. Thus, OSA in the context of SCD may
synergistically worsen cerebrovascular health. Despite this, there is a paucity of data describing
vasodilatory impairment in children with SCD who have co-existing OSA.
Previous studies assessing vascular reserve utilized cerebrovascular reactivity (CVR) to measure
vessel distensibility in the brain (Nur et al. 2009; Prohovnik et al. 2009). CVR can be obtained
with the use of blood-oxygen level dependent (BOLD) magnetic resonance imaging (MRI) in
67
conjunction with a CO2 gas challenge which artificially creates a hypercapnic condition. With
this method, the ability for brain microvasculature to react to a vasoactive stimulus can be
measured. The hypercapnic challenge acts as a stress test to observe the vasodilatory capacity of
the vessels which is a safe way to emulate situations when the vessels vasodilate to supply more
oxygen to the brain. Improved neuroimaging technology has enabled MRI to become a valuable
tool which can be used to improve our understanding of the events that occur within the brain
(Biswal et al. 2010). By observing children with SCD and co-existing OSA with neuroimaging
techniques, we may be able to identify those who may be at the most risk for a cerebrovascular
accident.
The purpose of this study is to determine if SCD children with OSA have reduced CVR
compared to SCD children with no OSA. We hypothesized that SCD children with OSA will
have reduced CVR compared to those without OSA due to the combined effect of anemia and
endothelial dysfunction on the cerebrovasculature. We also hypothesize that the severity of the
reductions will be correlated with polysomnography measures.
6.2 Methods
6.2.1 Subject recruitment
Twenty-three patients were recruited for this study between Dec. 2009 and Nov. 2014 after
Research and Ethics Board approval from the sleep clinic at the Hospital for Sick Children. For
our inclusion criteria, only the HbSS and HbSβ0 genotypes of SCD were included in the study.
The exclusion criteria for the study included history of psychological disease, major
cerebrovascular or cardiovascular disease. Patients on Tx and HU were included in the study.
The presence of OSA was confirmed through a polysomnography at the hospital for all
participants in the study. Among the twenty three, eight were diagnosed with OSA and fifteen
were diagnosed to be free from OSA. Age, sex, treatment and hematocrit levels were matched
between the groups. Patient demographics are shown on Table 6. Participants were asked to
68
refrain from consuming vasoactive substances such as caffeine or alcohol on the day of imaging.
Informed written consent was obtained from each subject or their parent/guardian.
Table 6 Patient demographics
OSA No-OSA
N 8 15
Age 14.4±1.84 14.2±2.17
Sex 1M 7F 6M 9F
AHI 3.18±1.24 0.467±0.567
BMI 21.08±2.82 19.04±2.94
Number of HU 0 3
Number of Tx 1 3
6.2.2 Polysomnography (PSG)
Patients underwent standard overnight PSG according to established international guidelines
using a XLTEK data acquisition and analysis system (Natus Medical, San Carlos, California).
All events were scored in accordance with the American Academy of Sleep Medicine scoring
guidelines. An Obstructive Apnea-Hypopnea Index (OAHI) of 1.5 or below was considered
normal, an OAHI of 1.5 to 5 indicated mild OSA, an OAHI between 5 and 10 indicated moderate
OSA, and an OAHI greater than 10 indicated severe OSA. All PSGs were interpreted by sleep
physicians at our institution.
6.2.3 Inducing end-tidal CO2 changes
The CO2 breathing challenge was identical to the one described in chapter 5.2.3 of the thesis
69
6.2.4 Magnetic resonance imaging
MRI protocols were identical to the one described in chapter 5.2.4
6.2.5 CVR data processing
MRI data processing was identical to the one described in chapter 5.2.5
6.2.6 Statistical analysis
All of the statistical analysis was performed on MATLAB. Global GM and WM CVR averages
and standard deviations were determined in the SCD group and the SCD-OSA group. The GM
and WM means were compared using the t-test (p < 0.05). Regional CVR averages and standard
deviations were determined using the AAL masks. For each AAL area defined, the mean OSA
group (defined as OAHI greater than 1.5) CVR value was compared against the mean No OSA
group (defined as OAHI below 1.5) CVR value using the student's t-test (with statistical
significance defined as p < 0.05). The correlational analysis between PSG measures and CVR
was performed by correlating PSG measures such as minimum oxygen saturations (SaO2-min) in
different stages of sleep (REM, NREM, total sleep time) with CVR using the Pearson
correlation.
6.3 Results
6.3.1 Patient recruitment
We collected data from 8 OSA patients and 15 no-OSA patients. After analysis, two OSA
subject data sets were discarded due to motion artifacts.
70
6.3.2 Global CVR comparisons between the OSA group and the no OSA
group
In our comparison of global CVR between the OSA and No-OSA group, it was observed that
SCD children with OSA had significantly reduced CVR in the GM (p < 0.05). However, CVR
was not seen to be significantly reduced in the WM although there was still a noticeable
reduction of CVR in the OSA group. These results are graphed in Figure 19.
Figure 19 Comparison of global CVR between OSA (red) and no-OSA SCD patients
71
6.3.3 Regional CVR comparisons between the OSA group and the no OSA
group
In our comparison of regional CVR between the OSA and No-OSA group, we observed that 55
out of 78 AAL areas showed significant CVR reductions in the OSA group compared to the No-
OSA group.
Figure 20 Regional CVR comparison between OSA (red) and No-OSA SCD patients. AAL2
(Right Precentral gyrus), AAL3 (Left Superior frontal gyrus), AAL8 (Right Middle frontal
gyrus), AAL36 (Right Posterior cingulate gyrus), AAL48 (Right Lingual gyrus), AAL53
(Left Inferior occipital gyrus)
Table 7 Regional CVR comparisons
No-OSA CVR
avg
No-OSA CVR
stdev
OSA CVR
avg
OSA CVR
stdev t-test p value
PreCG.R 0.133432 0.009085 0.085616 0.014503 0.015657
SFGdor.L 0.160466 0.013491 0.064117 0.027828 0.01051
72
MFG.R 0.136554 0.012504 0.081773 0.016909 0.020389
PCG.R 0.256895 0.022228 0.153121 0.033481 0.022586
PHG.L 0.144738 0.012847 0.079677 0.019071 0.013929
LING.L 0.223376 0.011596 0.132699 0.018848 0.001446
LING.R 0.24857 0.013782 0.151849 0.020987 0.00202
SOG.L 0.136804 0.012706 0.043809 0.027307 0.011743
SOG.R 0.155096 0.009864 0.075186 0.02396 0.012813
MOG.L 0.16675 0.00953 0.068963 0.029731 0.012263
MOG.R 0.159787 0.009959 0.089844 0.022269 0.018176
IOG.L 0.292859 0.024136 0.118394 0.0281 0.00039
IOG.R 0.267941 0.023842 0.151632 0.038779 0.031596
FFG.L 0.197991 0.015284 0.110299 0.021703 0.005474
FFG.R 0.204233 0.012325 0.131831 0.020451 0.009309
PCL.L 0.139193 0.015341 0.069297 0.028243 0.073148
PCL.R 0.180618 0.019034 0.06179 0.024079 0.001656
HES.R 0.160234 0.013052 0.107806 0.022979 0.071451
STG.L 0.177585 0.011512 0.107288 0.028876 0.049229
STG.R 0.173027 0.006544 0.116838 0.02006 0.027156
TPOsup.L 0.216334 0.018899 0.121543 0.038293 0.049513
TPOsup.R 0.175976 0.011779 0.100109 0.019427 0.005769
MTG.L 0.161581 0.008384 0.089676 0.028681 0.041975
MTG.R 0.175056 0.006922 0.108429 0.021539 0.017445
TPOmid.L 0.264867 0.030474 0.113547 0.028917 0.001934
TPOmid.R 0.228602 0.029472 0.084074 0.022813 0.00094
ITG.L 0.171528 0.017987 0.089488 0.029157 0.033272
ITG.R 0.166151 0.013616 0.101677 0.022563 0.03055
PreCG - precntral gyrus; SFG - superior frontal gyrus; dor – dorsolateral; MFG – middle frontal gyrus; PHG – parahippocampal gyrus; LING –
lingual gyrus; SOG – superior occipital gyrus; MOG – middle occipital gyrus; IOG – inferior occipital gyrus; FFG – fusiform gyrus; PCL –
paracentral lobule; HES – Heschl gyrus; STG – superior temporal gyrus; TPO – temporal pole; MTG – middle temporal gyrus; ITG – inferior
temporal gyrus; sup – superior, mid – middle; Units in %ΔMR/mmHgCO2
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6.3.4 Global association between CVR and PSG measures
In our association analysis, it was observed that GM and WM CVR were not significantly
associated with SaO2-min during REM sleep or SaO2-min across total sleep time when both the
OSA group and the no OSA group were included in the analysis. However, when the analysis
was performed separately on the groups, there was significant correlation between SaO2-min
during REM sleep and GM CVR in the OSA group. Similarly, significant associations were seen
between CVR and SaO2-min across total sleep time only in the OSA group. Interestingly, there
was a significant correlation between SaO2-min during NREM and GM/WM CVR in both the
OSA group and the No-OSA group.
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Figure 21 Global association between CVR and Total sleep time SaO2 in OSA patients
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6.3.5 Regional association between CVR and PSG measures
In the whole group regional association analysis (both OSA and No-OSA group), it was
observed that correlation with CVR and SaO2-min during NREM showed 25/78 areas with r >
0.5. No correlation was observed between CVR and SaO2-min during REM sleep or SaO2-min
across total sleep time. When the regional association analysis was performed within each group,
it was observed that CVR was significantly associated with SaO2-min during NREM in the OSA
group in 22/78 AAL areas, CVR was significantly associated with SaO2-min during REM in the
OSA group in 27/78 areas and CVR was significantly associated with SaO2-min across total
sleep time in the OSA group in 41/78 AAL areas. No regional associations between CVR and
SaO2-min were significant in the No-OSA group.
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Figure 22 Regional association between CVR and REM SaO2 in OSA patients for AAL 18
(rolandic operculum) and AAL62 (Right inferior parietal)
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Table 8 Regional correlation between nocturnal oxygenation and CVR
CVR vs NREM
(r value)
CVR vs REM
(r value)
CVR vs TST
(r value)
PreCG.L 0.876926 0.27258 0.504083
PreCG.R 0.949842 0.660757 0.641561
SFGdor.L 0.547996 0.624179 0.653299
SFGdor.R 0.367831 0.671118 0.759342
SFGorb.L 0.035567 0.771492 0.837854
SFGorb.R 0.170646 0.487032 0.937017
MFG.L 0.689638 0.509215 0.834446
MFG.R 0.4005 0.607124 0.945198
MFGorb.L 0.243125 0.76531 0.880227
MFGorb.R 0.232723 0.588133 0.72643
IFGoperc.L 0.878351 0.386911 0.837854
IFGoperc.R 0.720902 0.643739 0.692243
IFGtraing.L 0.773822 0.450777 0.778781
IFGtriang.R 0.790696 0.590169 0.946309
IFGorb.L 0.42391 0.543967 0.762299
IFGorb.R 0.332415 0.594559 0.706824
ROL.L 0.662495 0.289828 0.88244
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ROL.R 0.615061 0.956138 0.541479
SMA.L 0.728148 0.465081 0.68942
SMA.R 0.897329 0.789113 0.813327
OLF.L 0.76531 0.730274 0.708025
OLF.R 0.65284 0.041231 0.763151
SFGmed.L 0.36606 0.623217 0.27168
SFGmed.R 0.476655 0.775887 0.660303
SFGmedorb.L 0.286531 0.757628 0.398372
SFGmedorb.R 0.514587 0.65307 0.799937
REC.L 0.034293 0.607865 0.586686
REC.R 0.189868 0.349428 0.773175
INS.L 0.534883 0.026633 0.623057
INS.R 0.203789 0.276641 0.668581
ACG.L 0.62466 0.527257 0.754718
ACG.R 0.528488 0.472758 0.924932
DCG.L 0.638201 0.629921 0.602578
DCG.R 0.618142 0.315595 0.707814
PCG.L 0.654599 0.381182 0.35609
PCG.R 0.562583 0.150433 0.312026
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PHG.L 0.410731 0.191259 0.641171
PHG.R 0.204157 0.461411 0.020724
CAL.L 0.337194 0.052991 0.086585
CAL.R 0.488876 0.292438 0.057472
CUN.L 0.492037 0.127083 0.336898
CUN.R 0.320936 0.120291 0.281532
LING.L 0.330757 0.164438 0.22441
LING.R 0.31588 0.102323 0.119666
SOG.L 0.661211 0.147817 0.37027
SOG.R 0.363318 0.390128 0.056409
MOG.L 0.543507 0.192951 0.238202
MOG.R 0.33541 0.310982 0.031278
IOG.L 0.325576 0.111669 0.39
IOG.R 0.498999 0.63364 0.590254
FFG.L 0.277092 0.41328 0.708661
FFG.R 0.186279 0.71624 0.159154
PoCG.L 0.612944 0.376829 0.577148
PoCG.R 0.422137 0.828975 0.534041
SPG.L 0.565509 0.539259 0.465403
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SPG.R 0.377492 0.775048 0.240583
IPL.L 0.656354 0.448442 0.35805
IPL.R 0.501298 0.843149 0.586771
SMG.L 0.613025 0.374833 0.434971
SMG.R 0.47613 0.827345 0.724155
ANG.L 0.535817 0.731027 0.639453
ANG.R 0.249279 0.397744 0.19862
PCUN.L 0.652304 0.460435 0.639375
PCUN.R 0.436348 0.491223 0.207702
PCL.L 0.62498 0.664304 0.565685
PCL.R 0.467333 0.257391 0.37054
HES.L 0.213167 0.180582 0.77756
HES.R 0.181797 0.581893 0.18412
STG.L 0.472229 0.53066 0.706965
STG.R 0.440227 0.627057 0.850059
TPOsup.L 0.402616 0.603738 0.663777
TPOsup.R 0.21422 0.182483 0.601249
MTG.L 0.484252 0.371214 0.508429
MTG.R 0.389102 0.651613 0.653911
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TPOmid.L 0.385746 0.300183 0.460869
TPOmid.R 0.208902 0.215337 0.400375
ITG.L 0.352278 0.386005 0.742159
ITG.R 0.218403 0.532447 0.573411
PreCG - precntral gyrus; SFG - superior frontal gyrus; dor – dorsolateral; orb – orbital; MFG – middle frontal gyrus; IFG – inferior frontal gyrus;
operc – opercular; triang – triangular; ROL – rolandic; SMA – supplementary motor area; OLF – olfactory cortex; REC – gyrus rectus; INS –
insula; ACG – anterior cingulate gyrus; DCG – median cingulate gyrus; PCG – posterior cingulate gyrus; PHG – parahippocampal gyrus; CAL –
calcarine fissure; CUN – cuneus; LING – lingual gyrus; SOG – superior occipital gyrus; MOG – middle occipital gyrus; IOG – inferior occipital
gyrus; FFG – fusiform gyrus; SPG – superior parietal gyrus; PoCG – post central gyrus; IPL – inferior parietal gyrus; SMG – supramarginal
gyrus; ANG – angular gyrus; PCUN – precuneus; PCL – paracentral lobule; HES – Heschl gyrus; STG – superior temporal gyrus; TPO –
temporal pole; MTG – middle temporal gyrus; ITG – inferior temporal gyrus; sup – superior; mid – middle
6.4 Discussion
In this study, we used MRI-based measures of CVR to demonstrate that the presence of OSA can
significantly impair cerebrovascular health in children with SCD. Global CVR was significantly
reduced in the OSA group compared to the No-OSA group, while regional CVR was
significantly reduced in 55 out of the 78 AAL areas. The impairment in CVR was hypothesized
in previous studies to be an effect of OSA on the vasculature, with particular emphasis on its
contributions to endothelial dysfunction (Gozal et al. 2008; Urbano et al. 2008). It was
interesting that even mild OSA further reduced CVR in a population where CVR is low
compared to healthy individuals (Nur et al. 2009; Prohovnik et al. 2009). In a vulnerable
population such as children with SCD where stroke occurrence is already high, the addition of
OSA would further increase stroke risk by exacerbating the pre-existing vascular issues. As OSA
and SCD are both persistent conditions, the cerebrovascular system will continue to deteriorate
until it cannot meet the perfusion demands of the brain, leading to inevitable ischemic damage.
The regional associations between CVR and PSG measures were further proof that OSA
augments the adverse effect of SCD on the vasculature. When the analysis was performed on the
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group without OSA, there was no association found between CVR and SaO2-min levels in REM,
NREM and total sleep time (TST). However, in the OSA group, the association was significant
between global CVR and minimum oxygen saturation measures and it was also significant
between regional CVR and SaO2-min. The association analysis was especially interesting due to
the fact that there was a direct correlation between CVR, a measure of vascular health, and
nocturnal SaO2-min which demonstrated a possible direct effect between the state of the
vasculature and the severity of OSA. This correlation between nocturnal hypoxia and consequent
reduction in CVR supports the observations found in literature that suggest OSA contributes to
reductions in vasodilatory capacity by causing oxidative stress and subsequently endothelial
dysfunction. Most importantly, as this relationship was observed only in the group with OSA, it
could be deduced that the effect on the vasculature is not an aberrant situation created by the
presence of SCD in this population.
The regional association revealed several brain regions that were found to have greater CVR
impairment compared to other regions. The region which had the highest number of significant
correlations between nocturnal oxygen levels and CVR were the frontal lobes. This was a
significant finding since both SCD and OSA are independently associated with various cognitive
impairments (Kral et al. 2006; Canessa et al. 2011; Hijmans et al. 2011; Lal et al. 2012), most of
which are believed to be linked to the frontal lobe regions. Thus, the concomitant presence of
SCD and OSA could potentially work in conjunction to further worsen cognitive impairment by
adversely affecting the cerebrovasculature. In addition, reductions in regional CVR in SCD have
been associated with cortical thinning, thus neuronal loss in a particular brain region with
reduced CVR could be a realistic concern in the SCD population with OSA. Reductions in CVR
could further accelerate region neuronal loss which may lead to markedly poor cognitive
functioning in this population compared to those without OSA.
Future studies will need to incorporate cognitive data along with these results to determine if
observable physiological changes caused by the presence of OSA manifest as observable
behavioural changes. Although many factors such as age, sex and hematocrit were controlled for
in the study, SCD remains to be a widely variable disease and it is difficult to conclude the exact
extent to which OSA affects the vasculature in this population. Therefore, performing a
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longitudinal study in children with SCD and OSA could prove to be tremendously useful in
elucidating the long-term effects of OSA in this population.
6.5 Conclusion
In this study, it was demonstrated that CVR was significantly lower in the pediatric SCD
population with OSA compared to SCD patients without OSA. When CVR was associated with
PSG measures, it was observed that CVR was significantly associated with SaO2-min only in the
OSA group. This study demonstrated that the concomitant presence of SCD and OSA, even mild
OSA, could have a significant effect on the cerebral vasculature. Therefore, there should be
careful management of SCD patients diagnosed with OSA to ensure that their vasodilatory
capacity is not depleted. Such measures may be able to significantly reduce the risk of ischemic
events in the future.
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7 Discussion and conclusion
7.1 Overall discussion
In this project, cerebrovascular health of pediatric SCD patients was assessed using BOLD MRI
CVR. As hypothesized, pediatric patients with SCD exhibited reduced ability to vasodilate in the
brain compared to sex and age matched controls. Reductions in vasodilatory reserve were
observed both globally and regionally. Regionally specific reduction in CVR was a novel finding
which has not been previously observed. As such, we explored the implications of regional CVR
reductions by correlating CVR with measures of regional cortical thickness. This association
analysis revealed that regional reduction in CVR was associated with regional cortical thinning
and this association was present only in specific brain regions. These regions were regions of
high metabolic activity; as such these regions experienced the most severe thinning from
reductions in CVR as regional metabolic demands were potentially not being met on a daily
basis. This may result in neuronal cell loss or neuropil loss which was measured with cortical
thinning in our study. It was important to identify the regions which were potentially most
vulnerable to hypoxic damage since these brain regions could help to explain the mechanism
behind cognitive deficits observed in SCD without visible structural deficits on standard clinical
scans (Steen, Fineberg-Buchner, et al. 2005). Our study was able to identify one possible
mechanism in which this can occur and also identify which cognitive deficits are likely to
develop based on the most vulnerable regions. However, the exhaustion of vasodilatory capacity
may also have an effect on development throughout early childhood in addition to proactively
affecting brain structure even after active development. Thus it is possible that exhaustion of the
cerebral vasculature may lead to developmental delays in the brain since SCD children exhibit
delayed development (Soliman et al. 1999; Schatz and McClellan 2006; Martins et al. 2015). The
possible explanation for delayed development is that, systemic anemia is very strenuous on the
body and thus it cannot fully allocate nutrients and energy towards development. Furthermore, it
may be advantageous for individuals with SCD to have delayed for the vasculature to adapt to
these strenuous conditions.
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If the delay in development is mirrored in the brain, then it may be argued that reductions in
CVR may not lead to cortical thinning and cortical thinning which is observed is due to aberrant
development. However, this is an argument which cannot be proven without a longitudinal study
to monitor the typical development patterns in SCD. Furthermore, it would be useful to measure
regional oxygen metabolism (CMRO2) in children which allows us to study the relationship
between abnormal hemodynamics and brain structural changes. Having access to CMRO2 data
will allow us to verify if there is a change in CMRO2 in SCD due to changes in oxygenation,
CBF, CVR or OEF and if the metabolic demands of the region are not being met. Due to the fact
that stroke is the most devastating complication in SCD (Rees et al. 2010), a CMRO2 study
would be critical to the well-being of SCD patients. If stroke can be prevented through early
detection, it would change the current patient management in SCD. Thus reductions in
vasodilatory capacity and CMRO2 could be extremely important in learning about the stroke
pathophysiology in SCD.
If CVR and CMRO2 are instrumental in preventing complications in SCD, it is important to
prevent the drop of CVR and CMRO2 from occurring. As such we investigated the state of CVR
in patients with SCD who had concomitant OSA. We were able to observe that the presence of
OSA significantly reduced CVR globally and regionally in patients with SCD. Furthermore,
reductions in CVR were associated with the severity of the OSA as measured by nocturnal
oxygenation levels. The results of the study were able to affirm our initial hypothesis that the
simultaneous presentation of OSA in SCD children could significantly impact their
cerebrovascular health. The association analysis showed that reductions in CVR were associated
with minimum oxygen saturation only in the OSA group. This association indicated that level of
oxygen saturation during sleep in the SCD population could have a significant effect on the
cerebrovasculature. One possible mechanism which could lead to this is that when there is
reduced oxygen saturation during sleep, the adverse effects of systemic anemia are amplified
which leads to increased oxidative stress, inflammation and endothelial dysfunction (Lavie 2003;
de Montalembert et al. 2007; Gozal et al. 2007; Wood et al. 2008). The immediate consequence
of OSA on SCD could be manifested in several ways. The effect on the vasculature would not be
immediately apparent however other measures such as cognitive function can be quantified.
Previous literature has demonstrated that SCD and OSA are independently associated with
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cognitive deficits (Berkelhammer et al. 2007; Canessa et al. 2011; Hijmans et al. 2011; Edwards
et al. 2014). As such, when SCD patients suffer from OSA, their cognitive ability should be
reduced compared to those without OSA. This may be explained with the results from the
cortical thinning study which demonstrated regional associations between cortical thinning and
CVR reductions. Therefore, reductions in CVR in the OSA population could lead to brain
atrophy or abnormal brain development resulting in cognitive deficits. In addition, further
reductions in CVR may lead to increased risk for silent infarcts and white matter lesions in the
brain of SCD patients as more stress is levied on their cerebrovasculature. From our results it
was observed that even patients with only mild OSA (AHI < 5) had a noticeable reduction in
CVR. However, patients with mild OSA are not treated under the normal treatment paradigms.
Thus these patients may need to be treated with a special diagnosis and treatment regimen in the
future.
7.2 Limitations
One problem with our studies was the generalizability of our results to the SCD population due
to the heterogeneity of SCD patients especially in disease severity. The patient group in our
study was limited to HbSS patients without previous history of overt stroke and patients who
were not on Tx therapy following our closely controlled recruitment criteria. This allowed us to
reduce the number of confounders in our study however we were unable to capture the entire
spectrum of SCD since the patients we recruited were on the healthier end. There was also an
issue of having different treatment groups in the patient cohort (HU vs no HU). We included
patients on HU since they were quite numerous. Furthermore, the effects of HU on cortical
thickness is unknown thus there was no justification for excluding these patients in our study.
However, preliminary data from our lab suggests that HU significantly improves CVR by a small
amount which could have affected the results of the study. As such, the effects of HU on the
brain structure should be quantified in the future. There were also limitations in matching
appropriate controls for the study. Recruitment of controls for our study was limited to willing
participants from the community. As such, we could not control for factors such as
socioeconomic status and ethnicity. This limitation may have affected our results especially the
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CVR and cortical thickness comparisons between controls and SCD patients as the effect being
measured could have resulted from other factors than just the presence of SCD. However as the
association analysis demonstrated, significant association was observed only in the SCD group
and not in the control group thus the results were most likely derived from the presence of
disease. There was also an issue of unequal control subjects compared to SCD patients however,
we felt that this issue was addressed by the fact that there was less variability observed in the
control population in our CVR and cortical thickness data thus not as many were needed for our
purposes. Technically, there were concerns of partial volume effects for our CVR measures as
the voxel size was large. As a result, the reported measures of WM/GM CVR could have had
signal contributions from mixed sources. This could potentially increase measured CVR values
in WM and lower those in GM if the signal from GM/WM/CSF are not classified adequately
especially in regions where the different boundary regions. Additionally, there were concerns of
voxel size disparity between CVR and cortical thickness as our CVR voxel was much larger
compared to the cortical thickness voxels. As a result, there was a concern that surface CVR
could give us an inadequate value for an associational analysis with cortical thickness. We were
able to address this by adopting an approach inspired by TBSS (Smith et al. 2006) where we
sampled along a plane and take an average. This ensured that our CVR value would be able to
sample the value which was most likely to be grey matter.
In the OSA study, the main limitation of the study was that there were too few subjects to meet
the required power. Recruitment for the study was difficult even though a high percentage of the
SCD population had OSA due to the fact that it was usually very young subjects (age 2~4) who
were being diagnosed with OSA. Patients at this age usually cannot cope with our scan protocols
thus they were unavailable for the study. Furthermore, because the brains of these patients
develop at different rates compared to our population (age 10 ~ 18) (Toga et al. 2006; Shaw et al.
2008) it was best to recruit patients with comparable brain development trajectories. The patients
recruited in this study also had mild OSA and we did not recruit patients with severe OSA. Thus
the findings from the study were only applicable to SCD patients mild OSA. However due to the
fact that patients with severe OSA would be treated with CPAP or tonsillectomy, it made most
sense to observe the mild OSA patients. As with the SCD cortical thickness study, limited
number of patients led to the recruitment of patients on different treatments. Several patients
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were on HU which maybe have interacted with both OSA and SCD to affect the CVR measures
in these patients. However as the hematocrit was matched between the patient groups, the effect
of treatment on CVR was minimized in our study.
7.3 Future Directions
From our experimental findings, there are several directions for future studies. The cortical
thickness and CVR association study revealed several brain regions which had stronger
associations compared to others. Therefore, these regions should be the focal regions in future
studies. To investigate the neurodevelopmental trajectory of SCD patients, as well as to
determine the vascular contributions of brain abnormalities, a longitudinal study involving
regional cortical thickness and CVR will be necessary. By having measures of CVR and cortical
thickness across various time points, it is possible to observe how reductions in CVR and cortical
thickness transpire. This longitudinal measure of CVR and cortical thickness would be used to
elucidate if brain abnormalities in SCD are caused by hemodynamic changes or if the
abnormalities are a result of disrupted normal development. Furthermore, the high association
regions should be utilized as focal points when cognition is measured during the longitudinal
analysis. If specific cognitive impairments are strongly associated with reductions in regional
CVR and cortical thickness, it would indicate that structural abnormalities could be utilized as a
biomarker for specific cognitive deficits in SCD.
A MR based measure which could be instrumental to solve the question of brain structural
abnormalities and impaired neurocognition is regional measures of oxygen metabolism. It was
observed that CVR and cortical thickness was most strongly associated in the regions of high
metabolic demand, it was not clear if the reduced vasodilatory capacity was leading to a failure
to meet the metabolic needs of the brain regions which potentially led to severe cortical thinning
in these regions. To elucidate the mechanism behind metabolism, reduced vasodilatory capacity
and cortical thinning, regional measures of CMRO2 could be beneficial. Regional CMRO2
measures can be currently obtained using PET (Mintun et al. 1984) however PET requires
ionizing radiation which makes PET difficult to perform in pediatric research. The alternative is
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to measure oxygen metabolism with MRI (Fan et al. 2012) however, this method currently has
several limitations such as being limited to taking measurements mostly from large veins, having
poor spatial resolution and having a long acquisition time. Thus a new method of measuring
regional oxygen metabolism with MRI is necessary to accurately correlate them with our current
measures. With this new method we would be able to observe if adverse changes in vasculature
affects the regional CMRO2 in SCD and if the reduction in CMRO2 leads to brain abnormalities.
CMRO2 could also be utilized to determine stroke risk in children with SCD due to the fact that
oxygen extraction fraction (OEF) has long been thought of as a biomarker for determining
consequent stroke risk. Increases in OEF are believed to be associated with increased risk of
future stroke (Grubb et al. 1998) as increases in OEF signal impairments in normal perfusion. As
a more advanced measure compared to OEF, CMRO2 could be assumed to be a more direct
measure for detecting stroke risk. Furthermore, if there is a relationship between CMRO2 and
CVR it would allow us to determine if CVR could be utilized as a clinical tool for detecting
future stroke in children with SCD. Thus a study would need to be designed which would
correlate regional CMRO2 and CVR to establish the relationship between these two experimental
measures. In addition, a longitudinal study could be performed on patients with reduced CVR
and CMRO2 to determine if these patients undergo ischemic stroke in the future. If CVR would
be comparable to CMRO2 measures in predicting stroke risk in SCD children, then it may have
potential to be utilized as a clinical tool.
Previous MRI studies have focused on white matter abnormalities in SCD (Baldeweg et al. 2006;
Hogan et al. 2006) with DTI recently being utilized as an advanced imaging method for
investigating white matter abnormalities. As mentioned, studies have already demonstrated
abnormalities in DTI measures in the frontal lobes which were correlated with cognitive deficits
(Scantlebury et al. 2011); however, as with grey matter abnormalities, the cause of the white
matter abnormalities remain unknown. Therefore, to understand the mechanism behind white
matter abnormalities, the vascular contributions must be investigated. To investigate vascular
contributions, measures of regional CVR and CBF would be correlated with DTI measures such
as FA, mean diffusivity (MD) and apparent diffusion constant (ADC). This analysis would be
able to link vascular abnormalities with sub-clinical white matter abnormalities. In this type of
study, the regions of high association found in the cortical thickness/CVR association analysis
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could be used as ROIs to investigate white matter tracts involved with these regions.
Alternatively, predefined white matter tracts could be used as ROI using the tract based spatial
statistics analysis (TBSS) method (Smith et al. 2006). When values such as FA and MD are
obtained, they can be compared against healthy control values and correlated with regional white
matter CVR measures. If the cortical thinning/CVR study serves as an indication, we would
expect reduced FA and increased MD in the SCD group compared to healthy controls. We
should also expect there to be a regional relationship between reduced FA and reduced white
matter CVR in the SCD group which would demonstrate a vascular contribution to white matter
abnormalities. This study would be able to build on the current knowledge in the field on the
structural integrity of white matter tracts in SCD. The results from the study may also have
clinical significance. The degree of sub-clinical white matter abnormalities globally and
regionally may indicate higher risk of future silent infarcts (Scantlebury et al. 2011). In addition,
the regions with the highest correlation could be identified as regions most likely to suffer from
future silent infarcts. Thus patient care may have to be altered for these individuals who are
under the most risk for future silent infarcts. Furthermore, a connectivity based analysis can be
utilized to investigate the functional significance of structural and vascular abnormalities in the
white matter.
Investigating brain networks in SCD may enhance our understanding of neurological
development and cognitive development in children with SCD. As our study and previous
studies demonstrated GM and WM abnormalities in brain regions involved with various
networks such as the default mode network (Greicius et al. 2003; Fox et al. 2005; Biswal et al.
2010), a brain connectivity study combined with DTI and CVR measures could be useful in SCD.
Due to the widespread whiter matter and grey matter abnormalities, there is expected to be a
disturbance in the brain networks. This disturbance may result as increased activity or decreased
activity within the circuit. Having access to connectivity data could provide a mechanistic
understanding of brain function abnormalities in SCD specifically cognitive deficits in SCD.
Combined with the longitudinal WM/GM/CVR data, we may be able to observe how cognitive
deficits develop in SCD. Furthermore, connectivity analysis in combination with graph theory
based approaches (Zhou et al. 2014) may prove to be useful in identifying potential problem
areas of the brain.
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In the SCD patients with OSA, there are many avenues to explore due to the fact that there is a
lack of MRI data in the field. It was observed in our study that CVR was significantly reduced in
the OSA group compared to those without OSA however no other MR measures have been
quantified. Thus it would be logical to obtain advanced imaging measures of SCD patients with
OSA compared to those without OSA. This would include measuring cortical thickness, DTI
measures and CMRO2. Having access to these measures will be important in investigating the
effects of a concomitant disease which adversely affects the cerebrovasculature in children with
SCD. Measuring CMRO2 could especially be important in the SCD children with OSA due to the
fact that they are at high risk for stroke. If there were changes in CMRO2, this indicates that OSA
increases the risk of stroke. Thus a more aggressive approach will be necessary compared to the
current treatment paradigm for SCD patients with will be important in this cohort as OSA and
SCD diseases have been observed to be independently associated with cognitive deficits (Kral et
al. 2006; Hijmans et al. 2011; Miano et al. 2011; Edwards et al. 2014). By measuring the extent
of possible brain atrophy due to OSA, we can determine possible causes behind cognitive
deficits in this population. Specifically, if we observed deficits in the frontal cortex of the brain,
in terms of cortical thinning or white matter abnormalities, we may be able to explain deficits in
executive function.
Another study which can be performed in this population will be to investigate CVR before and
after treatment in the SCD patients with OSA. If CVR is closely related to stroke risk, then it is
of great interest to investigate if CVR changes after treatment in this population. If individuals
with severe OSA and SCD have permanent damage to their vasculature due to chronic hypoxia
combined with hemolytic complications, their CVR will not be restored to normal SCD values
even after treatment. However if the damage is not permanent, it is possible that CVR recovers
back to normal SCD values. In the case that there is no change in CVR after conventional OSA
treatment, the reasons why this occurred must be identified. CVR could remain the same after
treatment due to permanent damage to the endothelium. This would further strengthen the idea of
changing the patient treatment paradigm in SCD patients with mild OSA.
Additionally, SCD patients with severe OSA should also be considered in future studies. The
reason being is that the severe OSA patients are usually younger of age compared to the patients
92
who were recruited for our study. This means that the severe patients could be faced with a
higher risk of stroke (Adams 2007). Thus, when the younger patients suffer from severe OSA, it
may significantly increase the risk of stroke for this particular subset of SCD patients. To
investigate the risk of having stroke in younger SCD patients with severe OSA, CVR measures
could be compared against healthy controls as well as SCD patients with no-OSA similar to the
SCD OSA CVR study we performed. Currently, the treatment for SCD patients with severe OSA
would be adenotonsillectomy (Kaleyias et al. 2008) however, the wait time for the surgery can
be lengthy (up to 6 months, Ministry of Health 2015). This means that the younger SCD patients
with severe OSA could be faced with higher risk of ischemic events during the period between
diagnosis and treatment. Thus, measuring CVR may be an important first step to quantify the
effects of untreated severe OSA in young SCD patients. As a follow up of the study, CVR should
also be measured after the surgery and the change in CVR should be correlated against the length
`of time between diagnosis and surgery. The results from the study could reveal that shorter
intervals before surgery leads to higher increase in CVR post-treatment. Establishing the
physiological effects of severe OSA on the cerebrovasculature will be important as OSA is a
very common complication in SCD patients who already suffer from impaired cerebrovascular
health. As such further reductions in CVR due to severe OSA could potentially lead to serious
vascular complications in young SCD patients. Thus results from the study could potentially lead
to the shortening of surgery wait times for SCD patients.
7.4 Conclusion
In this thesis we were able to investigate the importance of cerebrovascular health in the
pediatric population with SCD. Maintaining cerebrovascular health in SCD remains to be a top
priority for care givers as poor vascular health leads to serious complications. However, patients
with SCD can also suffer from various complications which may be clinically asymptomatic as a
result of poor vascular health. These complications include endothelial dysfunction, cognitive
deficits and brain structural changes. Several studies have demonstrated brain structural
abnormalities in the GM and WM in SCD (Steen et al. 1999; Steen, Xiong, et al. 2003; Steen,
Emudianughe, et al. 2005; Kirk et al. 2009; Scantlebury et al. 2011) however, no study has been
93
able to establish a reason for these abnormalities in SCD. In our study, we were able to
investigate regionally specific association between vascular contributions and cortical thinning in
children with SCD. Experimental results have demonstrated that there was a strong regional
relationship between cortical thinning and CVR especially in regions of high metabolic activity.
Thus the results from the study were able to shed light on possible reasons behind brain
structural abnormalities in SCD. The regional association between CVR and cortical thinning
provided an additional reason for maintaining a good level of cerebrovascular health in SCD.
However in the presence of additional complications, cerebral hemodynamics could be adversely
affected in SCD. As such, we compared the CVR of SCD patients presented with OSA, a
common concomitant disorder, compared to those with no OSA. The results from the study
revealed that SCD patients with OSA had reduced CVR compared to SCD patients with no OSA.
Furthermore, the severity of OSA was seen to be associated with CVR reductions. These
findings demonstrated that the presence of an additional disorder that can impact vascular health
may have a significant impact on the cerebrovasculature in patients with SCD. Thus it may be
important to treat the OSA as soon as possible to prevent negative outcomes from compromised
cerebrovasculature especially in a patient cohort where the vasculature is already severely
compromised. From our experimental results, we assessed the state of vascular health in
children with SCD and we were able to highlight the importance of maintaining good vascular
health in this group of patients.
94
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