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Imaging Amnestic Mild Cognitive Impairment:
Neuroinflammation, Beta-Amyloid and Glutathione
by
Dunja Knezevic
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Medical Science University of Toronto
[11C]-PK11195 13 AD, 14 HV ↑frontal, temporal, parietal and occipital cortices, anterior and posterior cingulate, striatum No difference in hippocampus
First to study relationship ÆNo correlations found
Negative correlation with MMSE score
Yasuno et al., 2008
[11C]-DAA1106 10 AD, 10 HV ↑dorsal and medial prefrontal, lateral temporal, parietal and occipital cortices, anterior cingulate cortex, striatum, cerebellum No difference in posterior cingulate, medial temporal and thalamus
N/A No correlations found
Wiley et al., 2009
[11C]-PK11195 6 AD, 6 MCI, 5 HV
No difference between diagnostic groups No correlations found N/A
Yokokura et al., 2011
[11C]-PK11195 11 AD, 11 HV ↑medial frontal, parietal, and left temporal cortices, anterior and posterior cingulate No difference in hippocampus
N/A N/A Sample size, did not genotype participants
Kreisl et al., 2013
[11C]-PBR28 10aMCI; 4HAB, 6MAB
No difference Correlation in inferior parietal, superior temporal, precuneus, hippocampus, and parahippampal gyrus *only with PVEC
Negative correlation with MMSE, immediate memory, block design; positive correlation with CDR, trail making task B
Sample size, for correlations with cognition AD and aMCI patients were grouped
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1.6 Oxidative stress
Oxidative stress occurs from normal cellular and metabolic activities. However an
unconstrained generation of oxidative species is toxic and thought to be an important
pathogenic factor in schizophrenia, AD, Parkinson’s disease and amyotrophic lateral
sclerosis (Mandal et al. 2012). In particular, in AD it has been suggested that oxidative stress
plays a major role in the pathogenesis and progression of this disease.
1.6.1 Glutathione: The most abundant brain antioxidant
Although the mechanisms underpinning the increase in oxidative stress are unclear,
the antioxidant system is likely to be relevant, particularly glutathione (GSH), the brain’s
major antioxidant (Duffy et al. 2014). GSH is synthesized de novo in the brain and the supply
of GSH from other organs to the brain is restricted (Mandal et al. 2012). GSH plays an
important role in protecting the brain from oxidative damage induced by reactive oxygen
species (ROS). GSH is involved in a number of other essential tasks including DNA
synthesis and repair, protein synthesis, amino acid transport, enhancement of immune
function and enzyme activation (Bermejo et al. 2008). GSH protects cells from ROS damage
both non-enzymatically and enzymatically. For example, GSH reacts with the oxidant
hydrogen peroxide (H2O2) catalyzed by glutathione peroxidase (GPx) and coverts it to H2O
(Bermejo et al. 2008). During this process, GSH is oxidized to glutathione disulphide
(GSSG). The cycle is continued with GSSG being reduced back to GSH by glutathione
reductase (GR). Under physiological conditions, the levels of the reduced form of glutathione
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are 10-100 fold higher than the oxidized form. Interestingly, the distribution of GSH varies
by neuroanatomical areas (Venkateshappa et al. 2012). During oxidative stress, the ratio of
GSH/GSSG tends to be slightly reduced; nevertheless the cells are able to maintain their
glutathione redox state through different mechanisms (Bermejo et al. 2008;Ansari and Scheff
2010). However, when oxidative stress becomes prolonged, the cellular systems are no
longer able to counteract the ROS-mediated insults, leading to irreversible cell degeneration
and death. The reduction in GSH can be used as a good measure or indicator of oxidative
stress of an organism.
Figure 2. The glutathione reduction-oxidation cycle. Under normal physiological
conditions, GSH converts H2O2 to H2O. In this condition, levels of GSH are 10-100 fold
higher than the oxidized form, GSSG. During oxidative stress, this cycle maintains the redox
state of the cell. However, during pathological conditions such as AD, when oxidative stress
becomes prolonged, this balance can no longer be maintained and the levels of GSSG are
increased. The reduction of the GSH:GSSG ratio can be used as an indicator of the overall
oxidative stress of the animal.
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1.6.2 Oxidative stress and glutathione in AD and MCI
Oxidative stress has been shown to play a role in the pathogenesis of AD (Bermejo et
al. 2008). Post-mortem studies have shown oxidative modifications of DNA, RNA, lipids
and proteins in the brains of AD patients (Smith et al. 1991;Mecocci et al. 1994;Lovell et al.
1995;Good et al. 1996;Aksenov et al. 2001;Pamplona et al. 2005;Ansari and Scheff 2010).
Peripheral markers of oxidative stress have been studied in AD and MCI patients, in order to
understand the biochemical alterations in these stages. A commonly used marker of protein
oxidation are carbonyls, as these moieties are chemically stable, which is useful for their
detection and storage. Several studies have reported an increase in carbonyl groups in the
plasma of AD and MCI patients (Conrad et al. 2000;Choi et al. 2002;Bermejo et al. 2008).
One group that compared AD, MCI and healthy volunteers, found that the increase in the
level of carbonyls corresponded to the diagnostic group, meaning that AD patients had the
highest increase whereas MCI patients had an intermediate increase (Bermejo et al. 2008). In
addition to protein oxidation, markers of lipid oxidation have also been studied as indicators
of overall oxidative stress. Isoprostanes have been measured in the urine, blood and
cerebrospinal fluid of AD and MCI patients, with AD patients having the largest increase
(Tuppo et al. 2001;Pratico et al. 2002). However the data is not that concrete, as some groups
have reported no differences between patients and healthy volunteers (Montine et al. 2002).
In addition to the oxidation of proteins and lipids, GSH levels have been widely
studied in AD. Animal studies with 3xTg-AD mouse models have demonstrated that GSH
levels are reduced both in-vitro and in-vivo (Ghosh et al. 2012;Ghosh et al. 2014).
Reductions in GSH levels have also been reported in post-mortem human brain slices
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(Ramassamy et al. 2000;Venkateshappa et al. 2012) and in plasma samples of AD patients
(Bermejo et al. 2008;Puertas et al. 2012). In addition to GSH levels, reductions in the
GSH:GSSG ratio have also been measured (Bermejo et al. 2008;Cristalli et al. 2012).
However, because peripheral changes do not necessarily align with changes in the brain, a
direct association between AD and GSH levels has been elusive due to the limited amount of
in-vivo human studies (Mandal et al. 2015).
1.6.3 In-vivo quantification of GSH
1.6.3.1 Magnetic resonance spectroscopy
Magnetic resonance spectroscopy (MRS) is a non-invasive in-vivo method used to
measure key metabolites in the brain such as N-acetyl aspartate, glutamate (Glu), glutamine,
myo-inositol, choline, creatine (Cr) and glutathione (GSH). It provides a diagnostic tool for
the biochemical characterization of pathophysiological processes in the brain (Gujar et al.
2005). The quantification of these metabolites has been particularly useful in the study of
neurodegenerative disorders. Each metabolite can provide information about the underlying
degenerative process as metabolite levels are sensitive to different in-vivo pathological
processes at the molecular or cellular level (Marjanska et al. 2005). For example, N-acetyl
aspartate is proposed to be a putative marker of neuronal density (Metastasio et al. 2006))
and myo-inositol is thought to be a marker for osmotic stress or astrogliosis (Gujar et al.
2005).
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MRS is based on the chemical shift properties of a molecule. A variety of nuclei such
as carbon (13C), nitrogen (15N), fluorine (19F), sodium (23Na), phosphorus (31P) and hydrogen
(1H) can be used for the quantifications of metabolites in-vivo. However, only 31P and 1H
exist in high enough concentrations for useful clinical evaluation. 1H-MRS studies have
particularly become of interest due to the natural abundance of protons and their high
absolute sensitivity to magnetic manipulation, better spatial resolution, and relative
simplicity of technique (Gujar et al. 2005;van der Graaf 2010).
1.6.3.2 Measurement of GSH using MRS in AD and MCI
Due to the limited number of in-vivo human studies, there is a lack of understanding
of the impact of GSH reductions in AD (Mandal et al. 2015). Until recently, the
quantification of GSH using MRS has been elusive due to the low levels of brain GSH in
comparison to other brain metabolites. The first GSH MRS study with 45 young and 15 old
healthy volunteers, 11 MCI patients and 14 AD patients, found that GSH levels varied based
on the brain region, gender, age and diagnosis of the individual (Mandal et al. 2012). The
highest levels of GSH were reported in the parietal cortex compared to other brain regions.
When comparing the 4 groups, a trend in GSH levels was reported: young healthy > old
healthy > MCI > AD. However, a significant difference was obtained only between healthy
volunteers and AD patients. Subsequent larger MCI studies have found conflicting results
(Duffy et al. 2014;Mandal et al. 2015). One group found a significant elevation of GSH in
the anterior and posterior cingulate of patients in comparison to healthy volunteers (Duffy et
al. 2014). Furthermore, higher levels of GSH in the anterior cingulate were related to
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impairments on tests of executive function and elevated GSH in the posterior cingulate was
associated with poorer memory consolidation. The most recent study measured GSH levels
in the hippocampus and frontal cortex of AD and MCI patients, reductions were reported in
both regions of AD patients whereas MCI patients were reported to have a reduction in the
hippocampus only (Mandal et al. 2015). Overall, GSH in MCI is still not well understood
and further investigations of GSH are required to gain a better understanding of the
underlying pathology.
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Table 4. A summary of GSH MRS studies in AD and MCI populations.
Study Sample Regions Results Limitation
Mandal et al., 2012
11MCI 14AD 45 young HV 15 old HV
Frontal cortex Significant difference between young female HV and female AD patients in right frontal cortex Significant difference Between young male HV and male AD patients in left frontal cortex No difference between HV and MCI Trend in GSH levels Young HV>old HV>MCI>AD
Small sample size of patients
Duffy et al., 2014
54 MCI 42 HV
Anterior and posterior cingulate
MCI patients had significantly elevated GSH in both regions
MCI group includes those with naMCI and aMCI, used single-voxel PRESS sequence instead of MEGA-PRESS, cingulate is not as homogenous of a region as frontal cortex
Mandal et al., 2015
22MCI 21AD 28HV
Frontal cortex and hippocampus
AD patients had significantly reduced GSH levels in both regions MCI patients had significantly reduced GSH levels in hippocampus only
Hippocampal voxel contained some non-hippocampal tissue, frontal voxel contained a substantial fraction of WM
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2. AIMS AND HYPOTHESES
2.1 Aims
Neuroinflammation, as reviewed earlier, has been shown to play a role in the
pathology of AD. However, the exact timing and progression of this process is still unknown.
We sought to investigate whether neuroinflammation was present at an earlier stage of
cognitive impairment. Since individuals with early signs of cognitive impairment may
progress to different types of dementia, it was important to include patients who have an
underlying AD pathology. The amnestic subtype of MCI has been referred to as a prodromal
stage of AD, thus we pursued to measure neuroinflammation in these at risk individuals.
With the use of PET imaging and a novel TSPO radioligand [18F]-FEPPA, we investigated
whether neuroinflammation was elevated in patients compared to healthy volunteers.
Regions implicated with AD, such as the temporal cortex, inferior parietal cortex, temporal
cortex, occipital cortex, and hippocampus, were chosen to be investigated. As mentioned, the
binding of second-generation radioligands, such as [18F]-FEPPA, is affected by a single
nucleotide rs6971 polymorphism in the TSPO gene. In order to reduce the variability caused
by the differential binding, only HABs were included in the study. Thus far, PET studies
have reported conflicting results about whether neuroinflammation is present in the aMCI
stage. In order to gain a better understanding of the pathology, this study has several
novelties, including the use of a High-Resolution Research Tomograph (HRRT) scanner and
a novel second-generation radioligand, [18F]-FEPPA. In addition, this is the first study to
investigate neuroinflammation in a purely amnestic and HAB population of patients.
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Amyloid deposition, one of the hallmarks of AD, has been shown to be present in the
brains of aMCI patients as well. As reviewed earlier, several lines of evidence have
demonstrated a relationship between neuroinflammation and amyloid burden. However, the
in-vivo spatial relationship between the two processes still has not been well understood. Our
aim was to quantify amyloid burden in-vivo with the use of a validated radiotracer, [11C]-
PIB, and to explore whether there is a regional association between [18F]-FEPPA and [11C]-
PIB in aMCI patients.
Lastly, oxidative stress has been suggested to play a role in the pathogenesis and
progression of AD. The antioxidant system is particularly relevant when studying oxidative
stress, as disruptions in the balance of the oxidized to reduced form of GSH can be used as
an indicator of the oxidative state of an organism. The quantification of GSH with the use of
MRS has only recently become possible. We sought to explore whether there is an alteration
in GSH levels in the left dorsolateral prefrontal cortex (DLPFC) of aMCI patients compared
to controls. This region was chosen based on previous evidence demonstrating its role in AD.
Additionally, it has a good signal-to-noise ratio for MRS quantification. Given that the
DLPFC is an outcome measure for our PET data, correlations between [18F]-FEPPA, [11C]-
PIB and GSH were explored. This is the first study to quantify GSH in the left DLPFC of
aMCI patients, and to explore correlations with neuroinflammation and amyloid burden.
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2.2 Hypotheses Primary hypothesis: [18F]-FEPPA uptake will be greater in aMCI patients compared with age-matched HV.
Exploratory hypotheses: i) Regional [18F]-FEPPA uptake will show a positive association with [11C]-PIB
uptake. ii) GSH levels in the L DLPFC will be higher in aMCI patients compared with aged-
matched HV.
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3. METHODS
3.1 Participants
Individuals between the ages of 45 – 85 years old were recruited to participate in the
study. Participants with aMCI were recruited from the memory clinics at Baycrest Hospital
and the Centre for Addiction and Mental Health (CAMH), Toronto, Ontario. Healthy
volunteers were recruited from the Baycrest Research Participant Database and from local
advertisements. aMCI patients were diagnosed according to the Peterson et al. criteria for
aMCI (Petersen 2004). Briefly, the primary distinction between aMCI and HV participants is
in the area of memory, while other cognitive functions are comparable. The diagnosis of
aMCI was made on a clinical basis, established by a consensus committee comprising of a
neurologist, geriatric psychiatrists, neuropsychologist and other personnel working at the
memory clinics. Over 300 individuals were contacted for participation in the study, 83
individuals were interested and were screened over the phone. A total of 47 individuals met
criteria and were invited for the first visit whereby informed consent was obtained.
Additionally, blood samples were collected for genotyping of TSPO rs6971 polymorphism;
only HABs of both groups were invited to proceed with the study.
Eleven MCI patients and 14 healthy volunteers completed all study procedures: a
[18F]-FEPPA scan, [11C]-PIB scan and an MRI scan. All participants underwent a medical
and psychiatric assessment and a battery of neuropsychological tests. A urine drug screen
was also performed. The exclusion criteria for aMCI and healthy controls included: a current
Axis I disorder, history of closed head injuries with loss of consciousness, strokes, or other
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neurological disorders with central nervous system involvement. The protocol was approved
by the Research Ethics Boards of CAMH and Baycrest Health Sciences.
3.2 Neuropsychological assessments
During the baseline visit, several neuropsychological assessments were performed on
aMCI and healthy control participants. To evaluate the overall cognitive performance of
participants, the Mini-Mental State Examination (MMSE) was performed (Folstein et al.
1975). Since impairment in episodic memory is most commonly seen in MCI patients that
progress to AD, a variety of episodic memory tests that assess both immediate and delayed
memory recall were performed, including the Logical Memory II subscale from the Wechsler
Memory Scale-Revised (Wechsler 1987) and the Repeatable Battery for the Assessment of
Neuropsychological Status (RBANS) (Randolph et al. 1998). Given that other cognitive
domains may be affected in aMCI, additional tests were performed to assess language,
attention, executive function and visuospatial performance including RBANS, Montreal
Cognitive Assessment (MoCA) (Nasreddine et al. 2005), verbal fluency (Monsch et al.
1992), letter number span (LNS) (Wechsler et al. 2008), Stroop test (Spieler et al. 1996) and
trail-making test (Ashendorf et al. 2008). Premorbid intelligence was also assessed with the
North American Reading Test (NART) (Grober et al. 1991).
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3.3 PET measures of neuroinflammation and β-amyloid
plaques
The radiosynthesis of [18F]-FEPPA (Wilson et al. 2008) and [11C]-PIB (Mathis et al.
2002) have been described in detail elsewhere, and are currently synthesized at our CAMH-
Research Imaging Centre (CAMH-RIC).
3.3.1 PET image acquisition
PET images were obtained using a 3D High-Resolution Research Tomograph
(HRRT) scanner (CS/Siemens, Knoxvile, TN, USA). Prior to the start of the PET scans, a
custom fitted thermoplastic mask was made for each participant to minimize head
movement.
For [18F]-FEPPA, an intravenous saline solution of 4.91±0.42 mCi was administered
as a 1 minute bolus into the antecubital vein followed by 10 mL of saline. The scan duration
was 125 minutes following injection of the radiotracer. Blood samples were taken throughout
the scan to generate an input function for kinetic analysis (Rusjan et al. 2011). Since there is
no region in the brain that is completely void of TSPO binding, the plasma is used as a
reference. In order to measure radioactivity levels in the plasma, automatic and manual blood
samples were obtained. An automated blood sampling system (ABSS, Model #PBS-101
from Veenstra Instruments, Netherlands) was used to measure arterial blood radioactivity
continuously at a rate of 2.5 mL/minute for the first 22.5 minutes of the PET scan. Manual
arterial samples were obtained at 2.5, 7, 12, 15, 30, 45, 60, 90, and 120 minutes after the
injection of the radiotracer. From the blood samples, the following were determined: the
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amount of radioactivity in the whole blood, the blood-to-plasma ratio and the amount of
parent radioligand and metabolites. The blood curve was then divided by the bi-exponential
fitting of the blood-to-plasma ratio, and multiplied by the percentage of parent radiotracer to
generate a curve of the amount of parent compound in the plasma, which was then used as
the input function for the kinetic analysis. The images were reconstructed into a series of 34
time frames including 1 frame of variable length, followed by frames comprising 5×30
range: 56-78 years) completed all study procedures. All participants were high-affinity
binders. Descriptive characteristics and PET parameters are presented in Table 5. Diagnostic
groups were matched with regard to gender, age and education. In comparison to healthy
volunteers, aMCI participants had more cardiovascular risk factors such as high blood
pressure and elevated blood cholesterol level. Four aMCI participants were taking anti-
hypertensive drugs and 3 were on statins, whereas only 1 healthy volunteer was taking either
of these medications. Six of the 11 aMCI participants were taking anti-depressants, however
none met criteria for current diagnosis of major depressive disorder. aMCI participants
demonstrated an impairment in overall cognition, immediate and delayed memory,
visuospatial skills and executive function. None of the participants had any history of strokes
or other neurological disorders with central nervous system involvement.
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a. Sample size varies for cognitive scales. All 11 aMCI participants completed the MMSE, MoCA, Verbal Fluency, TMT and Logical Memory, the remaining cognitive scales were completed by 7 aMCI participants. For HV, MoCA scores are available for 14 participants; MMSE for 10; RBANS, Verbal Fluency and Trail Making for 9; Stroop for 8; Letter Number Span, Logical Memory and NART for 7. *significance is flagged for easy reference
Table 5. Participant demographics and PET parameters (mean±SD).
Visuospatial RBANS Visuospatial TMT – Task A (n=9)
-0.116 0.233
0.827 0.546
Executive Function TMT – Task B (n=9) Stroop Task – Color Score Stroop Task – Color Word Score
-0.050 0.655 0.943
0.898 0.158 0.005
Letter Number Span 0.507 0.305 Premorbid Intelligence
NART 0.086 0.872
Figure 18. Higher GSH levels correlated with a higher score on the Stroop Color Word
score. aMCI participants (n=6).
rho=0.943 p=0.005
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5. DISCUSSION
5.1 Increased amyloid in aMCI patients
Patients with aMCI had significantly more amyloid in the cortical regions, with the
highest amount in the prefrontal cortex followed by the temporal cortex, inferior parietal
cortex and occipital cortex. The hippocampus was the only region in which aMCI patients
did not have a significant increase of [11C]-PIB retention. Our observed distribution pattern
of [11C]-PIB retention is consistent with the pattern of Aβ plaque deposition observed in
post-mortem studies of the AD brain (Arnold et al. 1991;Thal et al. 2002), whereby studies
have shown large increases in neuritic plaques in cortical regions and low levels in the
medial temporal cortex, which includes the hippocampus. Furthermore PET imaging studies
of MCI and AD patients have similarly reported increases in [11C]-PIB retention in the
cortical regions and lower levels in the hippocampus (Klunk et al. 2004;Lopresti et al.
2005;Mintun et al. 2006;Kemppainen et al. 2007;Rowe et al. 2007;Edison et al.
2008;Forsberg et al. 2008;Okello et al. 2009;Wiley et al. 2009). In our aMCI sample, the
percent difference in [11C]-PIB retention between the two groups was 5% for the
hippocampus and 43% for the cortical regions. In an AD study, the difference in [11C]-PIB
retention in the hippocampus and cortical regions was 14% and 70-80%, respectively (Rowe
et al. 2007). Our lower uptake in comparison to AD studies supports the idea that PIB
retention in MCI patients is intermediate between healthy controls and AD patients (Forsberg
et al. 2008).
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The idea that aMCI patients fall into the intermediate range of PIB retention is further
supported by reports of patients being characterized as “PIB+” or “PIB-”. In our sample, 8/11
aMCI patients were characterized as PIB+ as indicated by an average cortical [11C]-PIB
DVR >1.20, whereas the remaining 3 aMCI patients were characterized as being PIB-. We
can speculate that these 3 PIB- patients may be on a different disease trajectory and may
develop another type of dementia other than AD. The presence of amyloid in the majority of
our aMCI patients (73%) is supported by other PET studies. Okello and colleagues, reported
7/14 aMCI participants as PIB+, with nearly two-fold increased uptake in the cingulate and
frontal regions (Okello et al. 2009). Likewise, Wiley and colleagues, reported 4/6 MCI
patients as being PIB+ and two-fold increases in the parietal, frontal, posterior cingulate, and
precuneus regions (Wiley et al. 2009). A larger study with 24 aMCI patients found that 18/24
patients (or 75%) were PIB+ (Rowe et al. 2007). Conversely, only 3/14 (or 21%) of our
healthy volunteers were characterized as PIB+, with the majority being classified as PIB-.
Increased amyloid deposition has been reported in up to 1/3 of cognitively normal elderly
participants (Mintun et al. 2006;Jack et al. 2008), which explains the finding of increased
[11C]-PIB binding in 3 of our healthy volunteers. In comparison to PIB- healthy volunteers,
those characterized as PIB+ did not demonstrate an impairment on any cognitive assessment.
Overall, the presence of amyloid in the majority of aMCI patients supports the idea that
amyloid deposition is an early event.
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5.2 No differences in [18F]-FEPPA VT
In contrast to amyloid pathology, we did not observe a significant difference in [18F]-
FEPPA binding between aMCI and healthy volunteers in any region of interest with either
the absolute quantification method (2TCM) or the supplementary SUVR method. PET
studies investigating neuroinflammation in MCI participants have observed conflicting
results, with some reporting no differences from controls (Wiley et al. 2009;Kreisl et al.
2013;Schuitemaker et al. 2013), while others reported increases in certain regions of interest
(Okello et al. 2009;Yasuno et al. 2012). It is important to note that there are notable
differences in the methodology and demographic characteristics between studies. Three of
the studies have used the prototypical radioligand, [11C]-PK11195, which as reviewed earlier
has several limitations including a short half-life, high nonspecific binding, low brain
penetration and high plasma protein binding (Okello et al. 2009;Wiley et al.
2009;Schuitemaker et al. 2013). Furthermore, two of the studies did not use a purely
amnestic sample of MCI patients (Wiley et al. 2009;Yasuno et al. 2012), which is a problem
as MCI is a broad category that may encompass a multitude of underlying causes. Thus these
two studies may not be a good comparative for us to use. In terms of studies that have
included aMCI patients specifically, one group observed an elevation in [11C]-PK11195
binding in patients with increased PIB retention (Okello et al. 2009). Similarly, in our aMCI
sample, those that were PIB+ had significantly higher [18F]-FEPPA binding in the prefrontal,
temporal, inferior parietal and occipital cortices compared to aMCI patients classified as
PIB-. However, when PIB+ aMCI were compared to PIB- healthy volunteers, no significant
differences were obtained. This may in part be explained by the variability in the arterial
input function. Two healthy volunteers had higher than expected K1 values (ratio of delivery)
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and ultimately higher VTs. Thus although the whole aMCI group did not have a significant
difference in [18F]-FEPPA binding, we can speculate that amyloid pathology may play a role
in the activation of microglia. This idea is consistent with immunohistochemical
examinations of brain slices that have revealed the presence of microglia surrounding Aβ
plaques in AD (Rogers et al. 1988;Itagaki et al. 1989;McGeer et al. 1989). However, a larger
cohort of patients is required to confirm whether those with increased amyloid have a parallel
increase in neuroinflammation. The most recent PET study with 10 aMCI patients, reported
similar group results as us, whereby they observed increases in [11C]-PIB retention but no
differences in [11C]-PBR28 binding between MCI patients and controls (Kreisl et al. 2013).
Thus from this second generation study and ours we can speculate that neuroinflammation
may only occur after conversion to AD. Another interesting speculation that arises from our
results is the relationship between tau and microglial activation. Intriguingly, the largest
difference in [18F]-FEPPA binding between aMCI and HV participants was in the
hippocampus, a region with earliest signs of tau accumulation (Small et al. 2006;Chien et al.
2013). Recently, in a mouse model it was shown that microglia play an important role in tau
propagation (Asai et al. 2015).Thus we can hypothesize that our finding of increased
microglial activation in the hippocampus is due to an increase in tau accumulation in this
region. This is further supported by the fact that the percent differences in [18F]-FEPPA
binding are low in the cortical regions, areas in which tau accumulates later on in disease
(Braak and Braak 1995).
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5.3 Exploratory correlations between amyloid and
neuroinflammation
In order to further investigate whether amyloid and neuroinflammation are regionally
associated, correlations between [11C]-PIB and [18F]-FEPPA binding were explored. In aMCI
patients, significant correlations between [11C]-PIB and [18F]-FEPPA binding were found in
the temporal cortex, prefrontal cortex, inferior parietal cortex, occipital cortex and
hippocampus. After correction for partial volume effects, the correlations remained in the
temporal cortex, prefrontal cortex and hippocampus. Our results are consistent with
histopathological studies that have demonstrated a colocalization of activated microglia with
Aβ-containing neuritic plaques in the AD brain (Rogers et al. 1988;Itagaki et al.
1989;McGeer et al. 1989). With Bonferroni correction for multiple comparisons, the
association between amyloid and neuroinflammation remained significant only in the
hippocampus. The relationship between amyloid burden and neuroinflammation has
previously been investigated in-vivo. The first PET study to investigate the spatial
relationship in AD patients did not find any significant correlations between [11C]-PIB and
[11C]-PK11195 in any region of interest (Edison et al. 2008). Similarly, two PET studies in
MCI patients did not find any regional associations between the two radioligands (Okello et
al. 2009;Wiley et al. 2009). The negative results observed by these previous studies may be
in part due to the use of [11C]-PK11195 and simplified reference tissue models rather than
the 2TCM. Thus far only one other study has evaluated the spatial relationship of amyloid
and neuroinflammation in AD and MCI with the use of a second generation radioligand and
the 2TCM. The study reported a significant correlation between [11C]-PBR28 and [11C]-PIB
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in the inferior parietal lobule, superior temporal cortex, precuneus, hippocampus and
parahippocampal gyrus (Kreisl et al. 2013). However, the correlations were observed only
after correction for partial volume effects. Overall, it is clear that the in-vivo spatial
relationship between amyloid and microglia is still not well understood, and a larger study is
required to elucidate the relationship.
5.4 Neuroinflammation, but not amyloid, may correlate with
cognition
In attempts to gain a better understanding of the underlying causes of impairment in
AD and MCI, studies have investigated whether pathologies such as amyloid and
neuroinflammation are associated with poorer performance on cognitive scales. Post-mortem
studies of AD have generally demonstrated that amyloid does not correlate well with
symptom severity or cognitive impairment (Arriagada et al. 1992;Bierer et al. 1995;Vehmas
et al. 2003), whereas in-vivo PET studies have reported conflicting results, with some groups
demonstrating no correlations between retention and performance on cognitive scales
(Edison et al. 2007;Jack et al. 2008;Okello et al. 2009), while others report correlations with
an impairment on episodic memory tests (Pike et al. 2007;Forsberg et al. 2008;Villemagne et
al. 2011). From our exploratory analyses, [11C]-PIB binding appeared to be associated with a
measure of episodic memory, the Logical Memory Delayed Task. The correlation suggests
that those with higher amyloid pathology have worse delayed memory recall. However, none
of the correlations between [11C]-PIB and memory survived Bonferroni correction for
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multiple comparisons, and thus we cannot consider them as true. Overall our result is
consistent with previous studies that have demonstrated that amyloid pathology is generally
not correlated with cognition.
Previous studies have shown conflicting results regarding the relationship between
neuroinflammation and cognition (Edison et al. 2008;Okello et al. 2009;Yokokura et al.
2011;Yasuno et al. 2012;Kreisl et al. 2013;Schuitemaker et al. 2013;Suridjan et al.
2015;Varrone et al. 2015). In MCI studies specifically, only one group has reported a
significant correlation with cognition and neuroinflammation (Kreisl et al. 2013). The study
reported strong correlations between [11C]-PBR28 binding in the inferior parietal lobule and
CDR score and performance on Block Design. However, the researchers did not consider the
results as true as they did not survive correction for partial volume effects. Furthermore, AD
patients were included in the correlation as well. Other MCI studies report no correlations
with MMSE (Okello et al. 2009;Yasuno et al. 2012) and a battery of other
neuropsychological assessments such as New York University Recall Test, Rey’s Auditory
Verbal Learning Test, Trail Making Test, Rey’s complex figure, Boston Naming Test, and
forward and backward condition of the Digit Span (Schuitemaker et al. 2013). In aMCI
patients, we observed negative correlations between Logical Memory Delayed Task and the
Stroop Color Word Task, however the only correlation to survive Bonferroni correction was
between [18F]-FEPPA binding in the hippocampus (after partial volume correction) and the
Logical Memory Delayed Task. Our finding suggests that those with higher microglial
activation in the hippocampus have worse delayed memory recall. Out of all cognitive scales
performed, an association with a measure of delayed memory is supported by the fact that
aMCI is characterized by a decline in episodic memory (Murphy et al. 2008). The conflicting
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results between cognition and neuroinflammation are in part due to methodological and
demographic differences between all studies. As already mentioned in previous sections,
several MCI TSPO studies have been performed with [11C]-PK11195, which has known
limitations (Okello et al. 2009;Wiley et al. 2009;Schuitemaker et al. 2013). Furthermore, two
of the PET studies only performed the MMSE rather than a battery of neuropsychological
assessments (Okello et al. 2009;Yasuno et al. 2012). Overall, future studies should be
performed with larger sample sizes and with a range of neuropsychological assessments to
better understand the association between neuroinflammation and cognition.
5.5 No differences in GSH levels
Although oxidative stress is thought to be an important feature of AD pathology,
whether GSH levels, the brain’s major antioxidant, are altered in-vivo is still not well
understood. Previous studies measuring GSH in AD and MCI populations have reported
conflicting results (Mandal et al. 2012;Duffy et al. 2014;Mandal et al. 2015). The first study
with young and old HV, MCI, and AD measured no differences between MCI and HV in the
frontal cortex (Mandal et al. 2012). However, they did report a trend in GSH levels: young
HV>old HV>MCI>AD. A subsequent study with a larger MCI sample observed a significant
elevation of GSH in the anterior and posterior cingulate (Duffy et al. 2014). Whereas the
most recent MCI study, observed a significant reduction in GSH in the hippocampus and
no differences in the frontal cortex (Mandal et al. 2015). Our study was the first to evaluate
GSH levels in the LDLPFC of aMCI patients and healthy volunteers. From the individuals
that had analyzable GSH data, no significant differences were observed between aMCI and
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healthy volunteers. A large variability in GSH levels was found in both groups, which may in
part be explained by a polymorphism in the gene coding for the catalytic (GCLC) subunit of
glutamate-cysteine ligase (GCL), the rate-limiting enzyme for GSH synthesis, which has
been reported to influence GSH concentrations (Xin et al. 2016). Similar to other MRS
studies, it is important to note that our voxel contained white and grey matter, however there
were no significant differences in the composition of the voxel between the two groups.
When comparing our results to other MCI MRS studies, certain caveats need to be
considered. Firstly, relating to the Duffy et al. study, the group did not have a purely
amnestic MCI sample. Additionally, the group used the PRESS pulse sequence which does
not distinguish GSH from overlapping brain metabolites as well as MEGA-PRESS (Mandal
et al. 2012). The latter study used the hippocampus as a region of interest, which is more
difficult to image using MRS because of its close proximity to ventricular cerebrospinal fluid
and low signal-to-noise ratio (Duffy et al. 2014). The lack of significant differences in GSH
levels in the LDLPFC of aMCI and healthy volunteers is supported by two earlier studies
that reported no difference in the full frontal cortex (Mandal et al. 2012;Mandal et al. 2015).
Other studies have suggested that GSH alterations are region-specific, thus we can speculate
that GSH levels in the DLPFC may remain unaffected. However, it is important to note that
we had a very small sample size, thus a larger sample is required to study GSH alterations in
prodromal AD patients. Future studies should include AD patients in order to study GSH on
a continuum from normal to aMCI to AD. Moreover, it would be beneficial to study GSH in
more than one region of interest, in attempts to better characterize the regional pattern of
GSH alterations in AD pathology.
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5.6 GSH levels correlate with microglia but not amyloid
In order to gain a better understanding of the underlying pathology of aMCI, we
explored potential correlations between GSH and our PET measures, amyloid and
neuroinflammation (Figure 19). This was the first study to investigate correlations between
GSH, amyloid and neuroinflammation in the in-vivo brain.
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Figure 19. Model of AD pathology demonstrating possible correlations between beta-
amyloid, neuroinflammation and oxidative stress. AD pathology is complex and includes
numerous pathological features; one idea of the interplay between these 3 pathologies is as
follows: Aβ has been shown to reduce GSH levels by modulating the synthesis of the
antioxidant (1). Although the exact mechanism is unknown, possible explanations include
the inhibition of a cysteine transporter and modulation of enzymes involved in GSH
synthesis (Mandal et al. 2015). Microglia, one of the major cellular drivers of
neuroinflammation, may be related to an increase in GSH (2). Chronic activation of
microglia has been shown to result in the secretion of ROS (McGeer and McGeer 2010).
Thus it can be postulated that GSH would be increased to reduce the oxidative species
produced (2).
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In aMCI patients, [11C]-PIB binding in the right, left and full DLPFC did not correlate
to GSH levels. All 3 regions were explored, as we assumed changes occur symmetrically
between the two hemispheres. No correlations were observed after partial volume correction.
Our results are in contrast to histopathological, animal and cell culture studies which have
demonstrated a correlation between amyloid accumulation and oxidative stress
(Anantharaman et al. 2006;Sultana et al. 2009). In a mouse model of AD, it was shown that
the overexpression of the amyloid precursor protein led to a decreased protein level of
EAAT3, the primary transporter of the rate-limiting amino acid in GSH synthesis, cysteine
(Nieoullon et al. 2006). Moreover, a cell culture study demonstrated the inhibition of
EAAT3 by Aβ oligomers (Hodgson et al. 2013).Thus it is expected that a negative
correlation between amyloid and GSH would have been observed. It is important to note that
our exploratory analyses between amyloid and GSH are underpowered and a larger sample
size is required to better understand whether there is a correlation between the two measures.
One additional possible explanation for the lack of correlation may be due to the fact that
[11C]-PIB binds to fibrillary amyloid beta aggregates and not soluble Aβ oligomers which are
more frequently correlated with GSH. In addition to amyloid, microglial activation can be
related to GSH levels. From our exploratory analyses, increased [18F]-FEPPA binding in the
left DLPFC and full DLPFC of aMCI patients was correlated with higher amounts of GSH.
Cell cultures studies have shown that highly activated microglia release free radicals (Boje
and Arora 1992;Chao et al. 1992;McGuire et al. 2001;McGeer and McGeer 2010). Thus one
plausible explanation for the positive association between [18F]-FEPPA and GSH is that the
brain is utilizing its antioxidant system to defend against free radicals produced by activated
93
microglia. The sample size for these exploratory correlations was small (9 aMCI), thus a
larger cohort of patients is required in order to confirm this relationship.
5.7 GSH and performance on neuropsychological tests
Two MRS studies in AD and MCI populations have reported correlations between
alterations in GSH levels and poorer performance on neuropsychological assessments (Duffy
et al. 2014;Mandal et al. 2015). In our sample of aMCI patients, higher levels of GSH were
correlated with better performance on an executive function task evaluating response
inhibition (Stroop). Our results are in contrast to another group that reported an association
between higher levels of GSH in the anterior cingulate and poorer performance on tests of set
shifting (TMT-B) and response inhibition (Stroop) (Duffy et al. 2014). The discrepancy
between the findings may be explained by the different brain regions assessed or differences
in pulse sequences used during acquisition. Additionally, our sample of patients is purely
amnestic, whereas the other study included both subtypes of MCI. A recent study with AD
and MCI patients reported an association between GSH reduction in the hippocampus and
frontal cortex and decline on global cognitive function, as measured by MMSE and CDR
(Mandal et al. 2015).This study suggests that less GSH is associated with an impairment,
which conversely might mean that more GSH is beneficial for cognition, as observed by our
exploratory correlations. Nevertheless, it should be noted that our sample size for the
correlation obtained was only 6 aMCI participants, thus a larger cohort of patients is required
to investigate correlations between GSH and cognition.
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6. STRENGTHS
It is important consider some of the strengths and novelties of our study. Firstly, we
were the first to evaluate amyloid, neuroinflammation and glutathione in-vivo. Furthermore
we were the first to include only a HAB population of participants. It has previously been
shown that [18F]-FEPPA VT is 30% higher in HAB healthy participants in comparison to
MABs (Mizrahi et al. 2012). Thus the inclusion of only HABs reduces the variability of the
sample. Recently, a study demonstrated that the pathologic features, clinical phenotypes and
rate of cognitive decline in AD and MCI were similar in all 3 TSPO genotypes (Fan et al.
2015). Additionally, another study reported no significant association of TSPO genotype
with either degree of cerebral amyloid angiopathy or microglial activation (Felsky et al.
2016). Taken together, these findings support our rational for inclusion of only HABs.
Our study also encompasses several methodological strengths. Firstly, we used a
high-resolution research tomograph (HRRT) scanner, which is a dedicated human brain PET
scanner with improved spatial resolution and sensitivity. For the quantification of TSPO, we
used a second-generation radioligand that has several advantages over the prototypical
radioligand [11C]-PK1195 including a longer half-life, higher affinity, lower metabolization
and easier preparation. Additionally, we obtained the arterial input function for absolute
quantification of TSPO binding. Unlike other multi-tracer PET studies, all of our participants
underwent [11C]-PIB scans and were included in the study regardless of their PIB status.
Lastly, with respect to the MRS portion of the study, we used a pulse sequence that has been
reported to be better in the quantification of GSH, MEGA-PRESS, and a region of interest
with a good signal-to-noise ratio
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7. LIMITATIONS
As with all imaging studies, certain limitations and confounding factors need to be
considered. Firstly, our sample size is small; however the inclusion of only HABs reduces
the variability of the sample. A caveat of TSPO PET studies is that radioligands bind to
TSPO expressed by astrocytes as well (Kreisl et al. 2013). Thus [18F]-FEPPA binding may
also be indicating the presence of reactive astrocytes in the brain, as currently there is no
direct evidence demonstrating that activated microglia are the main cellular source of [18F]-
FEPPA binding. However, a previous post-mortem study demonstrated that [3H]-PK11195
and [3H]-DAA1106 binding corresponded mainly to activated microglia (Venneti et al.
2008). Another consideration is that TSPO radioligands do not differentiate the two
phenotypes of microglia, M1 and M2, which are pro-inflammatory and neuroprotective,
respectively (Heneka et al. 2015). Perhaps in our elderly healthy volunteers, there are more
M2 microglia, whereas in the aMCI participants there are more M1 microglia, leading to an
overall similar level of microglial activation.
Although aMCI patients did not meet criteria for current Axis I disorder, 6 of the 11
participants were taking anti-depressants. There is some evidence that selective serotonin
reuptake inhibitors (SSRIs), selective norepinephrine reuptake inhibitors (SNRIs) and
tricyclic antidepressants (TCAs) can alter the inflammatory potential of microglia. Previous
studies have examined the ability of these anti-depressants to modulate microglial production
of cytokines (including TNF-α, IL-1β, IL-6) and the free radical nitric oxide (NO). Variable
results have been reported whereby some have reported no effects (Horikawa et al. 2010),
whereas others have reported an increase in production (Kubera et al. 2004;Ha et al.
96
2006;Tynan et al. 2012) or a decrease in production (Obuchowicz et al. 2006;Hashioka et al.
2007). Nevertheless, currently there is no evidence on the effect of SSRIs on TSPO
radioligand binding. A recent study investigating [18F]-FEPPA binding in AD, that similarly
included patients taking SSRIs, reported that differences in [18F]-FEPPA binding remained
significant in all GM and WM regions of interest even after excluding those on anti-
depressants (Suridjan et al. 2015). Similarly, within our sample, no differences in [18F]-
FEPPA binding were observed between patients on anti-depressants (n=6) when compared to
those not taking anti-depressants (n=5).
Other caveats should be considered in TSPO PET imaging studies. The lack of a
reference region (e.g. a region that does not have any specific binding) makes imaging TSPO
more difficult as arterial catheterization of the participant is required. Furthermore, arterial
sampling adds a potential source of error that may increase PET data variability (Lyoo et al.
2015). Recently a study investigating [11C]-PBR28 binding in AD and MCI participants
suggested that the cerebellum can be used as a pseudo-reference region and that the simple
ratio method may be more sensitive than the absolute quantitation method (Lyoo et al. 2015).
Our SUVR analysis was congruent with VT, whereby no significant differences were
observed between groups. Regarding the issue of possible errors in the arterial input
function, two of our healthy volunteers had high K1 values (ratio of delivery) and higher than
expected VT’s in regions of interest. The two participants were not taking any medications
and did not have any history of significant illness that may have contributed to the results.
Furthermore, neither of the individuals had an elevation in [11C]-PIB retention, thus we
cannot speculate that the increased microglial activation is due to amyloid pathology. When
97
the two healthy volunteers were removed from the analysis, our results remained the same,
with no significant differences between the two groups.
The in-vivo measurement of GSH has previously been difficult to quantify due to the
overlapping resonances of other brain metabolites. The MRS portion of our study is limited
by the small sample size. Additionally, we did not genotype our participants for the
polymorphism that was recently shown to influence GSH concentrations in-vivo. A GAG
trinucleotide repeat (TNR) polymorphism in the gene coding for the catalytic (GCLC)
subunit of glutamate-cysteine ligase (GCL), the rate-limiting enzyme for GSH synthesis, was
reported to influence GSH concentrations (Xin et al. 2016). This polymorphism may explain
the high variability in the GSH data. Similarly to other MRS studies, it is also to be noted
that our DLPFC voxels contained a fraction of white matter. Nevertheless, there were no
significant differences in the voxel composition between healthy volunteers and aMCI
participants. Finally, a caveat of MRS studies is that the technique does not differentiate
between GSH in neurons, glial cells or in extracellular pools, which may mask certain
differences between the two groups (Xin et al. 2016). Overall, a larger sample is required to
investigate whether GSH alterations are evident in this prodromal state.
.
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8. CONCLUSION
In summary, this was the first study to investigate amyloid burden,
neuroinflammation and GSH in the in-vivo brain of aMCI and healthy volunteers. Our
findings indicate that amyloid deposition is an early pathological event, as evidenced by
increased [11C]-PIB binding in the cortical regions of aMCI patients. The lack of significant
difference in [18F]-FEPPA binding between aMCI patients and healthy volunteers may
suggest that neuroinflammation occurs later during the progression to AD. On the contrary,
the lack of significant difference in [18F]-FEPPA binding may be due to the fact that
individuals with and without amyloid were included in both groups. This speculation is
supported by our findings of correlations between [18F]-FEPPA and [11C]-PIB, and by the
finding of increased [18F]-FEPPA in the subset of aMCI patients that were characterized to
be PIB+. A larger sample size will be required to confirm our exploratory correlations
between amyloid and neuroinflammation. Furthermore, our results also suggest that
neuroinflammation, but not amyloid, may be related to an impairment in episodic memory.
With respect to GSH, our findings indicate that there are no significant alterations in
GSH levels in aMCI patients. We were the first to explore possible correlations between
GSH, neuroinflammation, and amyloid. Our pilot data suggests that GSH is positively
correlated with neuroinflammation, but not amyloid, in the dorsolateral prefrontal cortex.
Finally, higher GSH levels may be related to better performance on an executive function
task, Stroop Color Word Test.
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9. FUTURE DIRECTIONS
Although neuroinflammation has been shown to be an important feature of AD
pathology, the timing and onset of this pathology is still not well understood. In order to gain
a better understanding a longitudinal [18F]-FEPPA study with aMCI and AD patients should
be performed to possibly elucidate the relationship between neuroinflammation and disease
progression. Moreover, this longitudinal study can include an additional group of
participants, those with subjective memory impairments but no diagnosis of aMCI, in order
to study the disease progression on a continuum from normal cognition to AD. Although the
inclusion of only one TSPO genetic group reduces the variability of the sample, and is one of
the strengths of our study, it makes recruitment much more difficult. Studies that may not
have access to large samples of patients may want to include participants that are MABs as
well.
To clarify associations between neuroinflammation and cognitive impairment, as
measured by neuropsychological assessments, large sample sizes are required. Furthermore,
future studies should include a battery of assessments, not just MMSE as is commonly seen
in PET studies of AD and MCI. Moreover, it would be interesting to include scores on
cognitive tests from healthy volunteers as well, in order to study correlations between
neuroinflammation and cognition on a continuum.
With respect to amyloid pathology, which has been demonstrated to be an early event
as observed by our PET study and others, it would be interesting to follow aMCI patients that
are both PIB- and PIB+ to investigate differences in neuroinflammation and conversion rates
to AD. Thus far there has only been one PET study that followed 5 MCI patients for 5 years
100
after their initial [11C]-DAA1106 scans (Yasuno et al. 2012). The group reported that all
subjects with initial [11C]-DAA1106 binding higher than control mean ±0.5 SD developed
dementia. This study however included MCI patients of both subtypes and did not evaluate
correlations with amyloid pathology.
Another important pathological feature of AD is the accumulation of neurofibrillary
tangles (NFTs) made up of hyperphospherylated tau (PHF-tau). Unlike amyloid-β plaques,
tau aggregates have been associated with cognitive decline and disease severity (Small et al.
2006;Chien et al. 2013). Tau deposition begins in a very limited area and then spreads as
clinical symptoms of dementia progress (Okamura et al. 2014). Moreover, the formations of
tau aggregates have been documented as preceding the cognitive symptoms of AD,
constituting them as a potentially reliable marker of early AD (Chien et al. 2013). In
comparison to amyloid, there are less PET studies that specifically target PHF-tau or NFTs.
The detection and better quantification of NFT burden may lead to potential therapeutics
down the line. Thus future studies should aim to image tau in aMCI and AD patients to better
characterize the underlying pathologies. Moreover, since neuroinflammation is thought to
aggregate tau pathology, a multi-tracer study can be performed with PET, whereby both
neuroinflammation and tau are quantified in-vivo (Heppner et al. 2015).
Given the complexity of AD, multi-modality imaging studies may be ideal in gaining
a better understanding of the different pathological features of this disease. For example,
future studies may want to investigate cortical thickness and structural atrophy, which can be
quantified with MRI, while imaging neuroinflammation with PET. These disease pathologies
can be measured over time to study progression. With the use of MRS brain metabolites,
indicative of atrophy, can be measured in combination with other PET or MRI measures.
101
Finally, in addition to the peripheral studies measuring markers of inflammation and
oxidative stress in the blood of AD and MCI patients, it would be interesting to correlate
measures in the blood to those in the brain. With regards to inflammation, a study can
investigate relationships between inflammatory cytokines and chemokines in the blood to
[18F]-FEPPA binding in the brain. With respect to oxidative stress, glutathione levels in the
blood can be correlated to glutathione levels in the brain with the use of MRS.
In sum, the use of multi-modality imaging techniques, including PET, MRI and
MRS, will allow for the quantification of different pathologies that characterize AD.
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