PET Imaging of Pathological Tau in Progressive

Supranuclear Palsy

By

Sarah Coakeley

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Institute of Medical Science

University of Toronto

© Copyright by Sarah Coakeley 2016

ii

PET Imaging of Pathological Tau in Progressive Supranuclear Palsy

Sarah Coakeley

Master of Science

Institute of Medical Science

University of Toronto

2016

Abstract

PSP is a neurodegenerative movement disorder that is characterized by the pathological

accumulation of tau aggregates in the brain. PSP differs neuropathologically from other

movement disorders such as PD and MSA, which are classified as synucleinopathies. PSP

patients would benefit from a clinically approved tau imaging agent for diagnostic and

prognostic purposes. Previous testing of the tau radiotracer [18F]AV-1451 in AD, another

tauopathy, suggested that it was capable of distinguishing AD from MCI and healthy

controls. The aim of this investigation was to test whether [18F]AV-1451 was able to

distinguish PSP patients from PD patients and healthy controls. There were no significant

increases in [18F]AV-1451 retention in PSP patients compared to the other two subject

groups. These findings may indicate that [18F]AV-1451 is not an effective radiotracer for

imaging tau in PSP, or perhaps the method of measuring [18F]AV-1451 retention used in

this study was not appropriate.

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Acknowledgments

My time at the University of Toronto has been filled with self-discovery and

independence. However, I would not have had the success and learning experiences I did

without the help of many incredible people. I have had the opportunity to collaborate with

amazing minds and work with state of the art technology, and for that I am truly grateful. I

would like to begin by acknowledging my mentor and supervisor, Dr. Antonio Strafella,

who provided me with the opportunity to complete my master’s thesis in his lab. Through

his guidance and example, Dr. Strafella has taught me what it means to be a diligent

researcher, a compassionate doctor, and a dedicated family man. Dr. Strafella has

provided me with exposure to the world of nuclear imaging and thereby the opportunity to

develop my analytical, technical, and personal skills. I am so thankful for the support,

training, and unique experiences I have been granted.

I would like to acknowledge the exceptional guidance, encouragement, and time

commitment provided by Dr. Ariel Graff-Guerrero and Dr. Robert Chen. Thank you to Dr.

Pablo Rusjan for your countless hours, tremendous guidance, and sharing of your

knowledge. Thank you to Alvina Ng, Laura Nguyen, Anusha Ravichandran and Dr.

Sylvain Houle for your much-appreciated support.

Thank you to the members of my lab for creating a cooperative and welcoming

environment. My days working in Dr. Strafella’s lab will be remembered fondly, not only

from the lessons I learned, but also the friends that I have made. Thank you to Sang Soo

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Cho for being so approachable and dedicated to teaching me about parametric analysis. I

will always appreciate your guidance and patience. Thank you to Leigh Christopher and

Yuko Koshimori for sharing your knowledge, offering advice, and teaching me the

importance of balance in my life. To Marc Jacobs and Marion Criaud, thank you for your

guidance, encouragement, and many laughs. Thank you to Christine Ghadery, Crystal Li,

Alex Mihaescu, and Rostom Mabrouk for your helpfulness, kindness, and friendships.

I would like to acknowledge the friendships, new and old, that have made my time as a

graduate student unforgettable. To Sam Fernandes, who came to U of T with me from our

undergraduate years, thank you for being an incredible friend, always willing to listen and

offer amazing advice. I am so thankful to have met Dunja Knezevic and Anton Rogachov

and cherish their support, humor, and friendship over the past two years. Thank you to

Zach Lister for your patience, motivation, and countless laughs along the way. I would

also like to acknowledge Shannen Busch, Alan Fauteux, and Lisa Shawcross for their

invaluable friendships and constant encouragement.

I would like to thank my family for their unyielding support and reassurance. To my dad,

who dedicated hours to grammatically correcting my manuscripts, and to my mom, who

always offered a considerate ear, thank you. I wish to acknowledge my siblings, Joey and

Elizabeth, for their comic relief and unparalleled hard work in their respective fields. I

know this work would not have been possible without my incredible family.

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Contributions

Dr. Antonio Strafella (Supervisor): provided laboratory resources, assistance with study

protocol, analysis of results, and guidance to manuscript writing

Dr. Ariel Graff-Guerrero: contributed to interpretation of results and guidance to

manuscript writing

Dr. Robert Chen: contributed to interpretation of results and guidance to manuscript

writing

Dr. Sang Soo Cho: provided assistance with analysis and direction of manuscript write up

Dr. Pablo Rusjan: provided assistance with analysis and direction of manuscript write up

Alvina Ng: reconstructed the individual PET images

Dr. Alan Wilson: provided expertise in radiosynthesis of [18F]AV-1451

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Table of Contents

Abstract………………………………………………………………………………. ii

Acknowledgements…………………………………………………………………... iii

Contributions…………………………………………………………………………. v

Table of Contents…………………………………………………………………….. vi

List of Tables………………………………………………………………………… x

List of Figures………………………………………………………………………... xi

List of Abbreviations………………………………………………………………… xv

1.0 Literature Review………………………………………………………………. 1

1.1 Proteinopathies…………………………………………………………………… 1

1.1.1 Tauopathies…………………………………………………………………….. 2

1.1.1.1 Alzheimer’s Disease…………………………………………………………. 4

1.1.1.2 Corticobasal Degeneration…………………………………………………… 5

1.1.1.3 Frontotemporal Dementia……………………………………………………. 6

1.1.1.4 Progressive Supranuclear Palsy……………………………………………… 7

1.1.2 Synucleinopathies……………………………………………………………… 7

1.1.2.1 Parkinson’s Disease………………………………………………………….. 8

1.1.2.2 Multiple System Atrophy…………………………………………………….. 9

1.2 Progressive Supranuclear Palsy………………………………………………….. 10

1.2.1 Clinical Presentations & Diagnosis …………………………………………… 10

1.2.2 Neuropathology………………………………………………………………… 11

1.2.3 Symptom Management & Treatment…………………………………………... 14

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1.3 Positron Emission Tomography………………………………………………….. 15

1.3.1 PET Tau Radiotracers………………………………………………………….. 16

1.3.1.1 FDDNP………………………………………………………………………. 17

1.3.1.2 THK………………………………………………………………………….. 20

1.3.1.3 PBB3…………………………………………………………………………. 22

1.3.1.4 T808………………………………………………………………………….. 23

1.3.1.5 AV-1451……………………………………………………………………... 25

1.3.1.5.1 Development & Preclinical Testing………………………………………... 25

1.3.1.5.2 Human Testing …………………………………………………………….. 27

1.3.1.5.3 Significance………………………………………………………………… 32

2.0 Aim & Hypothesis………………………………………………………………. 33

2.1 Aims……………………………………………………………………………… 33

2.2 Hypothesis……………………………………………………………………….. 34

3.0 Methods………………………………………………………………………….. 35

3.1 Participants & Experimental Design……………………………………………... 35

3.2 Radiosynthesis of [18F]AV-1451………………………………………………………. 38

3.3 MRI Acquisition…………………………………………………………………. 39

3.4 PET Acquisition………………………………………………………………….. 39

3.5 Image Analysis…………………………………………………………………… 40

3.5.1 Region of Interest Analysis…………………………………………………….. 40

3.5.2 Standard Uptake Value………………………………………………………… 41

3.5.3 [18F]AV-1451 Image Analysis………………………………………………………… 42

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3.6 Statistical Analysis……………………………………………………………….. 43

4.0 Results…………………………………………………………………………… 45

4.1 Participant Demographic………………………………………………………… 45

4.2 Region of Interest Analysis………………………………………………………. 47

4.2.1 Time-Activity Curves………………………………………………………….. 47

4.2.2 SUV……………………………………………………………………………. 51

4.2.3 SUVR Cerebellum……………………………………………………………... 57

4.2.4 Partial Volume Correction……………………………………………………... 75

4.2.5 SUVR Corpus Callosum………………………………………………………. 87

4.3 [18F]AV-1451 & MoCA…………………………………………………………………. 89

5.0 Discussion………………………………………………………………………... 90

5.1 Overviews of Findings…………………………………………………………… 90

5.1.1 Demographics………………………………………………………………….. 90

5.1.2 [18F]AV-1451 Retention……………………………………………………………….. 92

5.1.3 Reference Regions……………………………………………………………... 93

5.1.4 Atrophy & Partial Volume Correction………………………………………… 94

5.1.5 Off Target Binding……………………………………………………………... 96

5.2 Tau Radiotracers for PSP………………………………………………………… 96

5.3 Experimental Limitations ……………………………………………………….. 100

6.0 Conclusion ……………………………………………………………………… 103

7.0 Future Directions………………………………………………………………… 104

7.1 In Vitro Testing…………………………………………………………………... 104

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7.2 In Vivo Testing…………………………………………………………………… 105

7.3 Developing New Radiotracers…………………………………………………… 107

References……………………………………………………………………………. 109

x

List of Tables

Table 4-1. Participant demographics…………………………………………………………. 46 Table 4-2. Mean SUV (standard deviation) by ROI across groups from 30-60

minutes……………………………………………………………………………………….. 53 Table 4-3. Mean SUV (standard deviation) by ROI across groups from 60-90 minutes…….. 54 Table 4-4. Mean SUVR (standard deviation) from 30-60 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 59 Table 4-5. Mean SUVR (standard deviation) from 60-90 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 60 Table 4-6. Partial volume corrected mean SUVR (standard deviation) from 30-60 minutes

using the cerebellum as a reference region…………………………………………………… 76 Table 4-7. Partial volume corrected mean SUVR (standard deviation) from 60-90 minutes

using the cerebellum as a reference region…………………………………………………… 77 Table 4-8. Mean SUVR (standard deviation) from 30-60 minutes using the corpus callosum

as a reference region………………………………………………………………………….. 88

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List of Figures

Figure 1-1. Scoring of neuropathological accumulation of tau in PSP……………………….. 13 Figure 1-2. SUVR images from static scan (80-100 minutes)………………………………... 29 Figure 1-3. VOI SUVRs (80-100 minutes) for each subject…………………………………. 30 Figure 4-1. Time-activity curve of cerebellum and putamen in PSP subject………………… 48 Figure 4-2. Time-activity curve of cerebellum and putamen in PD subject………………….. 49 Figure 4-3. Time-activity curve of cerebellum and putamen in HC subject………………….. 50 Figure 4-4. Mean SUV of the cerebellum from 30-60 minutes………………………………. 55 Figure 4-5. Mean SUV of the cerebellum from 60-90 minutes………………………………. 55 Figure 4-6. Mean SUV of the corpus callosum from 30-60 minutes…………………………. 56 Figure 4-7. Mean SUV of the corpus callosum from 60-90 minutes…………………………. 56 Figure 4-8. Mean PSP group parametric image of mean SUVR 30-60 minutes……………... 61 Figure 4-9. Mean PSP group parametric image of mean SUVR 60-90 minutes……………... 61 Figure 4-10. Mean PD group parametric image of mean SUVR 30-60 minutes……………... 62 Figure 4-11. Mean PD group parametric image of mean SUVR 60-90 minutes……………... 62 Figure 4-12. Mean HC group parametric image of mean SUVR 30-60 minutes…………….. 63 Figure 4-13. Mean HC group parametric image of mean SUVR 60-90 minutes…………….. 63 Figure 4-14. Mean SUVRs of the frontal lobe from 30-60 minutes using the cerebellum as a

reference region………………………………………………………………………………. 64 Figure 4-15. Mean SUVRs of the frontal lobe from 60-90 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 64 Figure 4-16. Mean SUVRs of the inferior parietal lobe from 30-60 minutes using the

cerebellum as a reference region……………………………………………………………… 65

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Figure 4-17. Mean SUVRs of the inferior parietal lobe from 60-90 minutes using the

cerebellum as a reference region……………………………………………………………… 65 Figure 4-18. Mean SUVRs of the temporal lobe from 30-60 minutes using the cerebellum as

a reference region…………………………………………………………………………….. 66 Figure 4-19. Mean SUVRs of the temporal lobe from 60-90 minutes using the cerebellum as

a reference region……………………………………………………………………………... 66 Figure 4-20. Mean SUVRs of the occipital lobe from 30-60 minutes using the cerebellum as

a reference region…………………………………………………………………………….. 67 Figure 4-21. Mean SUVRs of the occipital lobe from 60-90 minutes using the cerebellum as

a reference region…………………………………………………………………………….. 67 Figure 4-22. Mean SUVRs of the caudate from 30-60 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 68 Figure 4-23. Mean SUVRs of the caudate from 60-90 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 68 Figure 4-24. Mean SUVRs of the putamen from 30-60 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 69 Figure 4-25. Mean SUVRs of the putamen from 60-90 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 69 Figure 4-26. Mean SUVRs of the striatum from 30-60 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 70 Figure 4-27. Mean SUVRs of the striatum from 60-90 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 70 Figure 4-28. Mean SUVRs of the globus pallidus from 30-60 minutes using the cerebellum

as a reference region………………………………………………………………………….. 71 Figure 4-29. Mean SUVRs of the globus pallidus from 60-90 minutes using the cerebellum

as a reference region………………………………………………………………………….. 71 Figure 4-30. Mean SUVRs of the substantia nigra from 30-60 minutes using the cerebellum

as a reference region………………………………………………………………………….. 72 Figure 4-31. Mean SUVRs of the substantia nigra from 60-90 minutes using the cerebellum

as a reference region………………………………………………………………………….. 72

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Figure 4-32. Mean SUVRs of the thalamus from 30-60 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 73 Figure 4-33. Mean SUVRs of the thalamus from 60-90 minutes using the cerebellum as a

reference region……………………………………………………………………………….. 73 Figure 4-34. Mean SUVRs of the dentate nucleus from 30-60 minutes using the cerebellum

as a reference region………………………………………………………………………….. 74 Figure 4-35. Mean SUVRs of the dentate nucleus from 60-90 minutes using the cerebellum

as a reference region………………………………………………………………………….. 74 Figure 4-36. Mean partial volume corrected SUVRs of the frontal lobe from 30-60 minutes

using the cerebellum as a reference region…………………………………………………… 78 Figure 4-37. Mean partial volume corrected SUVRs of the frontal lobe from 60-90 minutes

using the cerebellum as a reference region…………………………………………………… 78 Figure 4-38. Mean partial volume corrected SUVRs of the parietal lobe from 30-60 minutes

using the cerebellum as a reference region…………………………………………………… 79 Figure 4-39. Mean partial volume corrected SUVRs of the parietal lobe from 60-90 minutes

using the cerebellum as a reference region…………………………………………………… 79 Figure 4-40. Mean partial volume corrected SUVRs of the temporal lobe from 30-60

minutes using the cerebellum as a reference region………………………………………….. 80 Figure 4-41. Mean partial volume corrected SUVRs of the temporal lobe from 60-90

minutes using the cerebellum as a reference region………………………………………….. 80 Figure 4-42. Mean partial volume corrected SUVRs of the occipital lobe from 30-60

minutes using the cerebellum as a reference region………………………………………….. 81 Figure 4-43. Mean partial volume corrected SUVRs of the occipital lobe from 60-90

minutes using the cerebellum as a reference region………………………………………….. 81 Figure 4-44. Mean partial volume corrected SUVRs of the caudate from 30-60 minutes

using the cerebellum as a reference region…………………………………………………… 82 Figure 4-45. Mean partial volume corrected SUVRs of the caudate from 60-90 minutes

using the cerebellum as a reference region…………………………………………………… 82 Figure 4-46. Mean partial volume corrected SUVRs of the putamen from 30-60 minutes

using the cerebellum as a reference region…………………………………………………… 83

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Figure 4-47. Mean partial volume corrected SUVRs of the putamen from 60-90 minutes

using the cerebellum as a reference region…………………………………………………… 83 Figure 4-48. Mean partial volume corrected SUVRs of the globus pallidus from 30-60

minutes using the cerebellum as a reference region………………………………………….. 84 Figure 4-49. Mean partial volume corrected SUVRs of the globus pallidus from 60-90

minutes using the cerebellum as a reference region………………………………………….. 84 Figure 4-50. Mean partial volume corrected SUVRs of the substantia nigra from 30-60

minutes using the cerebellum as a reference region………………………………………….. 85 Figure 4-51. Mean partial volume corrected SUVRs of the substantia nigra from 60-90

minutes using the cerebellum as a reference region………………………………………….. 85 Figure 4-52. Mean partial volume corrected SUVRs of the thalamus from 30-60 minutes

using the cerebellum as a reference region…………………………………………………… 86 Figure 4-53. Mean partial volume corrected SUVRs of the thalamus from 60-90 minutes

using the cerebellum as a reference region…………………………………………………… 86 Figure 5-1. Diagram of tau band variance across different tauopathies……………………… 98 Figure 5-2. Electron micrographs of tau filaments…………………………………………… 99

xv

List of Abbreviations

AD: Alzheimer’s disease

ANOVA: analysis of variance

Aβ: β-amyloid

BBB: blood-brain barrier

BDI: Beck Depression Inventory

CBD: corticobasal degeneration

CNS: central nervous system

DLB: dementia with Lewy bodies

FDDNP: 2-(1-{6-{(2-[18F]fluoroethyl)(methyl)amino]-2-

naphthyl}ethylidene)malononitrile AD: Alzheimer’s disease

FDG: 2-[18F]fluoro-2-deoxy-D-glucose

FTD: frontotemporal dementia

ID: injected dose

MAP: microtubule associated protein

MAPT: microtubule associated protein tau

MCI: mild cognitive impairment

MDS: Movement Disorder Society

MMSE: Mini Mental State Examination

MoCA: Montreal Cognitive Assessment

MRI: magnetic resonance imaging

MW: body weight

NFT: neurofibrillary tangles

xvi

PBB: phenyl/pyridinyl-butadienyl-benzothiazoles/benzothiazoliums

PD: Parkinson’s disease

PET: positron emission tomography

PHF: paired helical filament

PIB: Pittsburgh Compound B

PiD: Pick’s disease

PNS: peripheral nervous system

PSP: progressive supranuclear palsy

PSPRS: Progressive Supranuclear Palsy Rating Scale

ROI: region of interest

RRT: relative residence time

SPM: Statistical Parametric Mapping

SUV: standard uptake value

SUVR: standard uptake value ratio

TAC: time-activity curve

THK: 6-(2-fluoroethoxy)-2-(4-aminophenyl)quinolone

UPDRS: Unified Parkinson’s Disease Rating Scale

VOI: volume of interest

1

1.0 LITERATURE REVIEW

Tau pathology is found in many neurodegenerative disorders, from dementias such as

Alzheimer’s disease to movement disorders such as progressive supranuclear palsy (PSP)

and corticobasal degeneration (CBD). Among these tauopathies there exists different

confirmations and distribution patterns of pathological tau. Tauopathies such as PSP may

have clinical signs that overlap with other Parkinsonian disorders; therefore, a method of

distinguishing between tauopathies and non-tauopathies would be very useful. Recently,

there has been an increased interest in developing positron emission tomography (PET)

radiotracers that bind tau in vivo.

1.1 Proteinopathies

Proteinopathies are a result of protein aggregation that is usually confined to the central

nervous system (CNS). These inclusions may be due to a number of factors, including

genetic mutation, altered post-translational modification, or atypical proteolysis (Wenning

& Jellinger, 2005). These abnormalities can change the folding and binding of a protein,

thereby altering its secondary or tertiary structure and ultimately the function of the

protein. Accumulation of protein aggregates over time can cause damage to susceptible

brain regions (Wenning & Jellinger, 2005).

2

1.1.1 Tauopathies

Tau is a microtubule-associated protein (MAP) found in the CNS and peripheral nervous

system (PNS). It is primarily located in the axons of neurons in healthy conditions (Avila,

Lucas, Perez, & Hernandez, 2004). Similarly to other MAPs, the role of tau is related to

microtubule assembly and stabilization (De Silva et al., 2003; Spillantini & Goedert,

2013). Tau is a natively unfolded, hydrophilic phosphoprotein with a long rod structure

and possesses a beta-sheet formation (Avila et al., 2004; Iqbal, Liu, Gong, & Grundke-

Iqbal, 2010; Spillantini & Goedert, 2013). The MAP tau (MAPT) gene is located on

chromosome 17q21.31 and alternative splicing of its mRNA yields six different isoforms

of tau (Shahani & Brandt, 2002; Spillantini & Goedert, 2013). Exons 2 and 3 can be

alternatively spliced to form isoforms with no amino terminal inserts (0N; lacking exons 2

and 3), one amino terminal insert (1N; lacking exon 3), and two amino terminal inserts

(2N) (Iqbal et al., 2010). Alternative splicing of exon 10 produces isoforms with 3 amino

acid repeats (3R) or 4 amino acid repeats (4R) on the carboxyl terminal. The repeats

contain tubulin and microtubule binding domains; therefore, the 4R isoforms have a

slightly higher affinity for microtubules than the 3R isoform (Iqbal et al., 2010; Shahani &

Brandt, 2002). In a human brain free of tau pathology the ratio of 3R to 4R tau isoforms is

approximately equal (Noble, Hanger, Miller, & Lovestone, 2013). Localization of tau in

the neuron is largely dependent on post-translational modifications, most notably

phosphorylation (Avila et al., 2004). Tau contains numerous serine and tyrosine

phosphorylation sites that allow its activity to be regulated. Phosphorylation of tau

decreases its affinity for microtubules, thereby decreasing overall tubulin assembly

3

(Shahani & Brandt, 2002). Phosphorylated tau is generally sequestered in the soma of

neurons, with small traces found in the nucleus – possibly involved in the regulation of

MAPT mRNA transcription. Non-phosphorylated tau is found in distal axonal regions of

neurons and is more prone to proteolysis than its phosphorylated counterpart (Avila et al.,

2004; Shahani & Brandt, 2002). Though the process of phosphorylation is reversible,

hyperphosphorylation of tau can disrupt the structure of neuronal microtubules and lead to

pathological conditions known as tauopathies (Avila et al., 2004; Noble et al., 2013).

Neurofibrillary tangles (NFT) are formed from phosphorylated tau aggregates and remain

intracellular, until the death of the neuron (Avila et al., 2004; Shahani & Brandt, 2002). In

many tauopathies tau are not confined to grey matter but also presents in glial cells,

namely astrocytes and oligodendrocytes.

There are many classes of tauopathies, all categorized by the accumulation of pathological

tau in the brain. Differences in tau isoforms, tau pathology, the distribution of

pathological tau within the CNS accounts for the varying symptom manifestations across

tauopathies; however, there is also frequent symptom overlap between many tauopathies.

The most common tauopathies include Alzheimer’s disease (AD), corticobasal

degeneration (CBD), frontotemporal dementia (FTD), and progressive supranuclear palsy

(PSP).

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1.1.1.1 Alzheimer’s Disease

AD is the most common cause of early-onset dementia and the most prominent symptom

is episodic memory impairment (Ahmed et al., 2014). This neurodegenerative disorder is

classified by beta amyloid (Aβ) plaques and NFTs. While plaque density and distribution

is not associated with cognitive decline, tau NFT burden has been found to be correlated

with neuronal death and cognitive impairment (Ludolph et al., 2009).

The initial development of tau NFTs in AD begins with the hyperphosphorylation of tau,

followed by the formation of paired helical filaments (PHF) of tau (Iqbal et al., 2010;

Lemoine et al., 2015; Thal, Attems, & Ewers, 2014). Braak and colleagues (1993)

developed a staging theory of cortical neurodegeneration in AD. Such theory has been

used to pathologically diagnose mild to severe AD. In the preclinical stages I and II there

are few to many NFTs in the transentorhinal region, which may extend to the entorhinal

region and hippocampus (Braak, Braak, & Bohl, 1993). Stages III and IV mark the

initiation of the clinical phase, with the presentation of mild to moderate cognitive

impairment. Stages III is distinguished by high levels of NTFs in the superficial entorhinal

layer. Involvement in the deep entorhinal cortex marks stage IV and affects the relay of

information from the hippocampus to the isocortex (Braak et al., 1993). In stages V and

VI, neurodegeneration is sufficient to obtain a pathological diagnosis of AD. Pathology

has spread to hippocampal areas and association regions of the isocortex (Braak et al.,

1993). NFT burden in the grey matter AD cortex is considerably greater than in other

tauopathies (Zhukareva et al., 2006).

5

All six tau isoforms are present in AD and there is an equal ratio of 3R to 4R isoforms (De

Silva et al., 2003; Iqbal et al., 2010; Spillantini & Goedert, 2013; Thal et al., 2014).

1.1.1.2 Corticobasal Degeneration

CBD is primarily a sporadic movement disorder that is difficult to diagnose clinically due

to its lack of specificity and clinical heterogeneity (Dickson, 1999; Grijalvo-Perez &

Litvan, 2014). Approximately 25-56% of cases are accurately diagnosed ante mortem

(Grijalvo-Perez & Litvan, 2014). Cardinal signs of CBD include asymmetrical apraxia,

rigidity, bradykinesia, and dystonia. Other clinical presentations consist of aphasia, alien

limb phenomenon, cortical sensory deficit, focal myoclonus, and a lack of response to

levodopa (M. J. Armstrong et al., 2013; Dickson, 1999; Grijalvo-Perez & Litvan, 2014;

Ludolph et al., 2009; Poewe & Wenning, 2002). Cognitive impairment can develop during

the disease progression, and may lead to a misdiagnosis of AD (M. J. Armstrong et al.,

2013; Ludolph et al., 2009).

Pathological diagnosis continues to be the gold standard for CBD, with tau aggregation

being the primary source of pathology (Grijalvo-Perez & Litvan, 2014; Stamelou &

Bhatia, 2015). The main tau isoform present in CBD tau inclusions is 4R (De Silva et al.,

2003; Dickson, 1999; Grijalvo-Perez & Litvan, 2014; Liscic, Srulijes, Gröger, Maetzler, &

Berg, 2013; Ludolph et al., 2009; Noble et al., 2013; Spillantini & Goedert, 2013; Thal et

6

al., 2014). Straight filament NFTs, astrocytic plaques, and coiled bodies often accumulate

in CBD in a clustered formation (R. A. Armstrong & Cairns, 2013; De Silva et al., 2003;

Takahashi, 2002). Asymmetrical frontal and parietal lobe atrophy is seen in CBD cases

(Ahmed et al., 2014; Poewe & Wenning, 2002). Atrophy can also be seen in the thalamus,

subthalamic nucleus, pallidum, dentate nucleus, and brainstem nuclei (Poewe & Wenning,

2002). The majority of CBD pathology is found in the grey and white matter of the cortex

(Dickson, 1999; McMillan et al., 2013).

1.1.1.3 Frontotemporal Dementia

Frontotemporal dementia (FTD) is the clinical term for a heterogeneous group of

syndromes that present with non-Alzheimer’s dementia (Warren, Rohrer, & Rossor,

2013). FTD is present in approximately 15 to 22 per 100,000. Other names that have been

replaced with FTD include Pick’s disease (PiD) and frontal lobe dementia of the non-

Alzheimer’s type. PiD is now only referred to cases with pathological confirmation of

Pick bodies, which are argyrophilic, tau inclusions (Pressman & Miller, 2013). The

primary tau isoforms in these aggregates is 3R (Arai et al., 2001; Spillantini & Goedert,

2013). FTD is divided into three clinical subgroups: behavioural variant frontotemporal

dementia (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), and semantic

variant primary progressive aphasia (svPPA) (Pressman & Miller, 2013; Warren et al.,

2013). BvFTD is the most common subtype and is marked by a decline in executive skills,

7

abnormal behaviour and differed emotional responses. NfvPPA is categorized by impaired

speech and language decline, and svPPA is marked by a decline in word comprehension

and semantic memory (Warren et al., 2013). Pathologically, FTD is associated with three

types of protein inclusions, the most common being hyperphosphorylated tau. Mutations

in the MAPT gene have been found in approximately 17-32% of cases (Pressman &

Miller, 2013).

1.1.1.4 Progressive Supranuclear Palsy (see section 1.2)

1.1.2 Synucleinopathies

α-synuclein is a presynaptic, 140 amino acid protein whose function is not well

understood (Goedert, Trans, Lond, & Goedert, 1999; Wenning & Jellinger, 2005). While

there are two other proteins in the synuclein family (β and γ) only α-synuclein forms

aggregates in pathological conditions. Genetic mutations, oxidative stress, and

environmental factors may trigger or exacerbate the formation of α-synuclein fibrils

(Goedert et al., 1999; Wenning & Jellinger, 2005). The most common neurodegenerative

disorders associated with α-synuclein aggregation (α-synucleinopathies) are Parkinson’s

disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB)

(Wenning & Jellinger, 2005).

8

1.1.2.1 Parkinson’s Disease

PD is a neurodegenerative movement disorder affecting 1% of North Americans over the

age of 60 (De Lau & Breteler, 2006). In most cases PD is sporadic, with certain

environment elements and aging being risk factors; however, seven genes have been

linked to familial parkinsonism (Lees, Hardy, & Revesz, 2009). The cardinal signs of PD

are bradykinesia, resting tremor, rigidity, and an excellent response to levodopa, which is

currently the most effective therapy for PD (Goedert et al., 1999; Lees et al., 2009). Early

signs of PD include impaired dexterity, fatigue, stiffness, instability, and falls. Urinary

incontinence may develop later on in the disease course, along with monotonous speech

and freezing of gait. PD progression is generally slow and unilateral (Lees et al., 2009).

Pathologically, PD is distinguished by Lewy bodies, pale bodies, and Lewy neurites. α-

synuclein inclusions are the main components of Lewy bodies and neurites (Goedert et al.,

1999; Lees et al., 2009). Loss of dopaminergic neurons and depigmentation is seen in the

pars compact of the substantia nigra. Atrophy can be present in the locus coeruleus, dorsal

nucleus of the vagus, and the raphe nuclei (Lees et al., 2009).

9

1.1.2.2 Multiple System Atrophy

MSA is rare atypical parkinsonian syndrome, with a prevalence of approximately 1.9-4.9

cases per 100,000 (Wenning, Colosimo, Geser, & Poewe, 2004). There are two clinical

subtypes of MSA: MSA-P which is dominated by Parkinsonian features, and MSA-C with

cerebellar ataxia as the primary motor presentation (Poewe & Wenning, 2002; Wenning et

al., 2004). The clinical features of MSA-P include akinesia, rigidity, and orofacial

dystonia (Poewe & Wenning, 2002). Less common is postural instability that lead to falls,

similar to PSP (Liscic et al., 2013; Wenning et al., 2004). MSA-C presents with gait

ataxia, limb kinetic ataxia, scanning dysarthria, and cerebellar oculomotor disturbances.

MSA patients often do not respond well to levodopa medications (Poewe & Wenning,

2002). Although clinical diagnosis has relatively good specificity, it lacks sensitivity and

many cases of MSA are misdiagnosed or undiagnosed (Wenning et al., 2004).

Pathologically, MSA is characterized by the aggregation of α-synuclein (Poewe &

Wenning, 2002; Wenning et al., 2004). Inclusions can be found in neurons and glial cells,

with depigmentation of the substantia nigra and atrophy of the putamen (Wenning et al.,

2004).

10

1.2 Progressive Supranuclear Palsy

Progressive Supranuclear Paly (PSP) is the second most common atypical parkinsonian

disorder, affecting approximately 5-6/100,000 North Americans (Golbe, 2014b; Liscic et

al., 2013). This neurodegenerative disorder often presents in middle to late age (55 to 70

years) and disease duration is approximately 4 to 5.5 years (Osaki et al., 2004).

1.2.1 Clinical Presentations & Diagnosis of Progressive

Supranuclear Palsy

Onset of PSP symptoms is insidious, the earliest sign of PSP is most commonly

unexplained falls, followed by deteriorating postural instability. The second most

common clinical manifestation of PSP is dysarthria (Litvan et al., 1996). Approximately

60% of cases present with gait difficulty, and bradykinesia similar to PD is also frequent

(Golbe, 2014a; Litvan et al., 1996). The hallmark of PSP is supranuclear vertical gaze

palsy and horizontal gaze may be affected later on in the disease course (Liscic et al.,

2013; Litvan et al., 1996). Cognitive changes and eventually dementia can appear later on

in the disease progression (Kobylecki et al., 2015; Litvan et al., 1996).

Misdiagnosis is a common problem in PSP and up to 50% of cases are misdiagnosed or

undiagnosed. The most common misdiagnosis of PSP is PD (Osaki et al., 2004).

11

1.2.2 Neuropathology of Progressive Supranuclear Palsy

Tau aggregates are the main pathology in PSP; therefore, it is classified as a primary

tauopathy. Tau inclusions can accumulate in both neurons and glial cells. In neurons,

NFTs present as straight filaments and are formed primarily by 4R tau inclusions (Arai et

al., 2001; Dickson, 1999; Zhukareva et al., 2006). Aggregation also occurs in glial cells –

in astrocytes these are termed ‘tufted astrocytes’ and in oligodendrocytes ‘coiled bodies’.

The regional distribution of lesions is not clustered and generally presents as random

dispersions (R. A. Armstrong & Cairns, 2013; Tawanna & Ramsden, 2001). The majority

of NFT tau pathology is situated in subcortical regions, primarily in the basal ganglia, with

involvement specifically in the striatum, caudate, putamen, subthalamic nucleus,

substantia nigra and also dentate nucleus of the cerebellum (R. A. Armstrong & Cairns,

2013; Togo & Dickson, 2002; Wray, Saxton, Anderton, & Hanger, 2008). Brainstem

areas commonly implicated include the pontine nuclei and tegmentum, the locus

coeruleus, oculomotor nuclei, the periaqueductal grey matter and the superior colliculus

(Dickson, 1999). Tau pathology may also be present in the premotor and motor cortices

(R. A. Armstrong & Cairns, 2013). Tufted astrocytes are primarily seen in the frontal lobe,

putamen and cerebellar white matter, and may have cognitive implications (Tawanna &

Ramsden, 2001; Zhukareva et al., 2006). Williams and colleagues (2007) established a

scoring system (12 points) that grades the severity of PSP based on tau pathology in the

substantia nigra, caudate, and dentate nucleus. See Figure 1-1.

12

Atrophy primarily occurs in the majorly affected midbrain and subcortical regions.

Midbrain atrophy is very common and can be accompanied by atrophy of the aqueduct of

Sylvius (Dickson, 1999; Tawanna & Ramsden, 2001). The subthamalic nucleus, globus

pallidus and the ventrolateral substantia nigra present with the brunt of neuronal loss.

Depigmentation is seen in the substantia nigra and locus coeruleus (Dickson, 1999). The

dentate nucleus, superior cerebellar peduncle, and brainstem tegmentum may also appear

smaller. Up to 40% of the striatum has been reported to undergo neurodegeneration.

Cortical atrophy may also occur, specifically in the frontal cortex spanning to the pre-

central gyrus (R. A. Armstrong & Cairns, 2013; Dickson, 1999; Tawanna & Ramsden,

2001).

13

Figure 1-1. Scoring of neuropathological accumulation of tau in PSP. Legend (A).

Scoring 0-1 (B): minor involvement of the basal ganglia and pre-motor cortex. Scores 2-3

14

(C): increased pathology in the basal ganglia, pontine nuclei, and dentate nucleus. Scores

4-5 (D): severity increased in basal ganglia and dentate nucleus, increased involvement in

white matter of the cerebellum and frontal and parietal lobes. Scores 6-7 (E): moderately

severe involvement in the substantia nigra, globus pallidus, subthalamic nucleus, pontine

nuclei and white matter of the cerebellum, increased involvement in frontal and parietal

lobes. Scores >7 (F): severe pathology in subthalamic nucleus, increased involvement in

neocortical regions. Reproduced with permission from (Williams et al., 2007).

1.2.3 Treatment & Symptom Management of Progressive

Supranuclear Palsy

Currently there are no therapies for PSP symptom management, or treatments to reduce

tau burden or prevent further accumulation. Levodopa and other pharmaceuticals that act

on the dopaminergic system, as well as the cholinergic and GABAergic systems,

historically do not generally aid with PSP symptoms (Koros & Stamelou, 2016). In

attempt to address tau burden in tauopathies, inhibition of tau phosphorylation has been of

large interest recently (Noble et al., 2013). Tideglusib, a glycogen synthase kinase 3 (GSK

3) inhibitor, was tested in PSP in an effort to reduce tau hyperphosphorylation. There was

no significant difference between the tideglusib groups and the placebo group in terms of

the PSP rating scale; however, patients in the drug groups showed slowed atrophy

(Hoglinger et al., 2014). Other efforts have been made in animal studies to develop

15

microtubule stabilizing therapeutics, drugs targeting heat—shock proteins, and tau

aggregation inhibitors (Gerson, Castillo-Carranza, & Kayed, 2014; Schroeder, Joly-

Amado, Gordon, & Morgan, 2015). Investigations into active and passive tau

immunization are also currently being formed on animal models (Schroeder et al., 2015).

1.3 Positron Emission Tomography

Positron emission tomography (PET) is a non-invasive imaging modality that uses

radioligands (or radiotracers) to detect targets in vivo. Ligands are typically labeled with

either fluorine-18 or carbon-11, radioisotopes with relatively short half-lives (110 minutes

and 20 minutes, respectively). Following a venous injection, the radiotracer enters the

bloodstream and is transported to the brain (or any other target organ/region) where it

interacts with its target protein. The radioligand’s unstable nucleus undergoes positron

decay and when the emitted positron collides with a neighbouring electron an annihilation

event occurs. The result of an annihilation event is the release of two photons of equal

energy (511 keV) in opposite directions (exactly 180º from one another). These photons

are detected by scintillation detectors that line the PET scanner in a cylindrical

configuration. Detection of two photons within a coincidence window (~ 6 nsec) results in

a coincidence event that is plotted on a sinogram. After the scan is complete, the sinogram

undergoes scatter correction and attenuation correction, using the brief transmission prior

16

to the emission scan. The PET image is reconstructed based on the sinogram plots and

radioligand dead time and decay correction should be performed.

1.3.3 Positron Emission Tomography Tau Radiotracers

Brain PET radiotracers must meet many of the same requirements of drugs with targets in

the brain, in order to passively cross the blood brain barrier (BBB) and be effective as

neuroimaging agents. Lipophilicity is one of the most important factors that determines

whether or not a radioligand will successfully cross the BBB. A LogP of approximately

2.0-3.5 is optimal because the compound is lipophilic enough to rapidly cross the bilipid

membrane, but not so lipophilic that the compound binds to plasma proteins, P-

glycoprotein, or other nonspecific targets (Pike, 2010; Shah & Catafau, 2014; Villemagne

& Okamura, 2014). Another condition for passively crossing the BBB is a low molecular

weight (<500Da) (Pike, 2010). An optimal radiotracer will have rapid uptake and washout

from the brain. For the majority of effective radioligands 5% of the injected dose is taken

up into the brain 2 to 5 minute post-injection (Villemagne & Okamura, 2014). It is

important that the metabolism of a radiotracer occur outside the brain and that the

metabolites are less lipophilic than the parent compound to avoid nonspecific binding in

the brain and thus, greater noise (Pike, 2010; Shah & Catafau, 2014).

17

Tau as a radioligand target poses many challenges. It is an intracellular protein; therefore,

the radiotracer must not only be capable of passing through the BBB, but also neuronal

and glial plasma membranes (Shah & Catafau, 2014; Villemagne & Okamura, 2014). Due

to the various concentrations of tau in different brain regions, an ideal radiotracer would

have very high affinity for tau in order to limit the amount of radioactivity that must be

injected into the subject. The tau radioligand should have a very high specificity for tau

because it is often co-localized with other proteins that have β-sheet secondary structures

(Shah & Catafau, 2014; Villemagne & Okamura, 2014). Due to the involvement of

pathological tau in glial cells, the radioligand should also have very low nonspecific

binding in white matter (Shah & Catafau, 2014).

1.3.2.1 FDDNP

The radiotracer 18F-(2-(1-{6-{(2-[18F]fluoroethyl)(methyl)amino]-2-

naphthyl}ethylidene)malononitrile) ([18F]FDDNP) was developed as the first non-invasive

biomarker to image NFTs and Aβ plaques in the brains of AD patients (Shoghi-Jadid et

al., 2002). The first human testing of [18F]FDDNP occurred in 9 AD subjects (7 probable

AD, 2 possible AD) and 16 neurologically healthy controls. Participants also underwent a

2-[18F]fluoro-2-deoxy-D-glucose (FDG) PET scan to test for glucose metabolism across

brain regions. ROIs were delineated manually and relative residence time (RRT) was

calculated for each ROI. This study demonstrated that the [18F]FDDNP parent compound

crossed the BBB quite rapidly and appeared to be metabolized peripherally with no signs

18

of metabolites crossing the BBB (Shoghi-Jadid et al., 2002). [18F]FDDNP binding

appeared to be inversely correlated with glucose metabolism. Using autoradiographic

methods, it was determined that [18F]FDDNP binding in vivo was consistent with

immunohistochemical staining of NFTs and Aβ. Interestingly, [18F]FDDNP was inversely

correlated with participants’ cognitive scores (Shoghi-Jadid et al., 2002). These results are

consistent with previous findings that tau is associated with cognitive decline (Ludolph et

al., 2009). Although this was a small sample size and kinetic modeling is required to

validate the distribution of [18F]FDDNP, this preliminary study demonstrated the

radiotracer’s ability to label NFTs and Aβ in vivo in AD.

A later [18F]FDDNP study included AD patients (n=25), MCI patients (n=28), and healthy

control participants (n=30) (Small et al., 2006). MCI patients exhibited significantly

increased uptake of [18F]FDDNP compared to healthy controls, while uptake in AD

patients was significantly higher than both MCI and healthy controls (Small et al., 2006).

Areas with particularly high uptake included the frontal lobe, temporal lobe, parietal lobe,

and posterior cingulate. These regions are known to have tangle and plaque involvement

in AD. A neuropathological evaluation was performed on one participant who passed

away 14 months following baseline testing. It was found that areas high in [18F]FDDNP

binding were associated with high NFTs and plaque immunoreactivity. Particularly the

hippocampus and entorhinal cortex, which were high in NFT concentration, displayed

high [18F]FDDNP binding. This study supports previous findings in human [18F]FDDNP

studies and suggests that this radiotracer is capable of distinguishing AD, MCI, and

healthy controls (Small et al., 2006).

19

A review written by Shin and colleagues (2011) outlines the utility and limitations of

imaging AD using [18F]FDDNP. This radiotracer is capable of labelling tau inclusion in

MAPT transgenic mice, expressing human pathological tau. When tested in MCI and AD

subjects, [18F]FDDNP signal was correlated with both Mini Mental State Examination

(MMSE) scores and AD brain pathology progression described by Braak & Braak (1993)

(Shin, Kepe, Barrio, & Small, 2011). Furthermore, when AD patients were imaged with

[18F]FDDNP and [11C]PIB, higher binding of [18F]FDDNP but not [11C]PIB was seen in

the medial temporal cortex, a region associated with memory and cognitive impairment in

AD. These findings support the theory that tau aggregate accumulation is associated with

cognitive decline. Patients with frontotemporal dementia also showed increased uptake of

[18F-FDDNP. In PSP, [18F]-FDDNP binding was seen in the caudate, putamen, thalamus,

and cortical regions as disease severity increased (Shin et al., 2011).

The most recent [18F]FDDNP study performed with PSP recruited 15 PSP, 9 PD, and 5

healthy age-matched controls (Kepe et al., 2013). All participants were imaged for 65

minutes following a [18F]FDDNP injection. Results demonstrated significantly increased

binding of [18F]FDDNP in the midbrain, subthalamic region, and cerebellar white matter

in PSP patients. Additionally, patients with more severe PSP had greater [18F]FDDNP

binding in cortical regions (Kepe et al., 2013). These subcortical regions are consistent

with PSP pathology and cortical involvement is indicative of PSP progression. The

primary concern with imaging PSP using [18F]FDDNP is its lack of specificity for tau over

other proteins with β-sheet structures.

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1.3.2.2 THK

A group of tau radiotracers have been developed from arylquinone derivatives. The first

in its group to be tested was [18F]THK-523. Fodero-Tavoletti and colleagues (2011)

investigated the appropriateness of [18F]THK-523 as a PET radiotracer and its ability to

detect tau pathology with high affinity and selectivity. Favourable properties of

[18F]THK-523 as a radiotracer include low molecular weight, capability of being labeled

with 18F at a high specific radioactivity, and an acceptable lipophilicity to cross the BBB.

In vitro binding studies using AD and age-matched control brain slices and in vivo

transgenic mice studies demonstrated that [18F]THK-523 selectively bound tau deposits

over Aβ plaques (Fodero-Tavoletti et al., 2011; Harada et al., 2013). However, testing in

non-AD tauopathy brain slices (PiD, PSP, and CBD) revealed no THK523 fluorescence

(Fodero-Tavoletti & Furumoto, 2014). Lack of binding in these straight filament

tauopathies may be due to a specificity of THK-523 for PHF tau conformation.

Two derivatives of THK-523, THK-5105 and THK-5117, were optimized for PET use and

tested in vitro in AD brain slices (Okamura et al., 2013). Fluorescent tissue staining and

autoradiography revealed that THK-5105 and THK5117 labeling of NFTs in the

hippocampus was consistent with immunohistochemistry results. There were significantly

different binding patterns of [18F]THK-5105 and [18F]THK-5117 compared to [11C]PIB,

suggesting that the arylquinone derivatives do not exhibit Aβ plaque binding (Okamura et

al., 2013).

21

In a clinical study the uptakes of [18F]THK5117 and [11C]PIB were compared in 8 AD

patients and 6 age-matched controls (Harada et al., 2015). SUVRs were calculated for

both radiotracers using the cerebellar cortex as a reference region. Regional distribution

of [18F]THK-5117 differed from that of [11C]PIB, indicating that [18F]THK-5117 binding

does not reflect Aβ localization (Harada et al., 2015). The [18F]THK-5117 SUVRs of the

temporal cortex were significantly higher in AD patients compared to healthy controls. As

previously mentioned, high pathological tau load in the temporal cortex is associated with

cognitive decline. [18F]THK-5117 demonstrated excellent pharmacokinetics, with rapid

entry into the brain and reaching a plateau in healthy controls around 50 minutes post-

injection. However, there was substantial retention in the subcortical white matter, which

may represent off-target binding (Harada et al., 2015).

A longitudinal [18F]THK-5117 study was performed by Ishiki and colleagues (2015) to

track pathological tau in 5 cases of AD. Patients were diagnosed based on the National

Institute of Neurological and Communicative Disorders and Stroke, and the AD Related

Disorders Association’s criteria. All AD patients and 5 age-matched controls underwent a

[11C]PIB scan to test for Aβ deposits. Healthy controls did not show plaques on their PIB

scan. A following [18F]THK-5117 scan was performed and volumes of interest (VOIs)

were delineated automatically using PMOD software (Ishiki et al., 2015). Baseline scans

revealed significantly higher SUVRs in the temporal lobes of AD patients compared to

healthy controls. Additionally, regional distribution of [18F]THK-5117 in AD patients

was consistent with previous post-mortem findings. Annual changes in the retention of

[18F]THK-5117 in AD brains were seen in the middle and inferior temporal gyri and the

22

fusiform gyrus, suggesting that tau pathology originates in the medial temporal cortex and

accumulates laterally (Ishiki et al., 2015).

1.3.2.3 PBB3

Phenyl/pyridinyl-butadienyl-benzothiazoles/benzothiazoliums (PBBs) are a group of

compounds developed for tau labeling based on fluorescent screening for β-sheet binding

capability (Maruyama et al., 2013). Testing in the brain stem of PS19 mice (4R isoform

mutation) demonstrated the ability of PBBs to bind tau positive NFTs. Furthermore,

fluorescent microscopy confirmed the high affinity of PBBs for tau aggregates (Maruyama

et al., 2013). Additional optimization yielded [11C]PBB3 for PET use. Using PS19 mice

and microPET [11C]PBB3 rapidly crossed the BBB and bound tau inclusions. Retention

was significantly greater in PS19 mice compared to age-matched control mice (Maruyama

et al., 2013). The first human testing of [11C]PBB3 was measured in comparison to

[11C]PIB signal. The SUVR retention patterns of [11C]PBB3 differed significantly from

that of [11C]PIB, suggesting that [11C]PBB3 binds tau selectively over Aβ (Maruyama et

al., 2013). Accumulation of [11C]PBB3 was seen in the medial and lateral temporal

cortices, and the frontal cortex – consistent with the Braak Staging Theory (Braak et al.,

1993; Maruyama et al., 2013). When tested in a corticobasal syndrome patient [11C]PBB3

retention was high in the neocortex and subcortical structures (Maruyama et al., 2013).

23

A recent clinical trial was performed on 7 AD patients and 7 healthy control participants

(Kimura et al., 2015). Participants underwent both [11C]PBB3 and [11C]PIB scans on the

same day. All AD subjects were Aβ positive and all healthy controls were Aβ negative.

Arterial samples were taken during the [11C]PBB3 scans which revealed rapid metabolism

of the parent compound to a more lipophilic metabolite that has been shown to cross the

BBB in mouse brains (Kimura et al., 2015). As previously mentioned, entry of a

metabolite into the brain decreases the signal to noise ratio.

1.3.2.4 T808

In attempt to develop a PET radiotracer designed to bind PHF tau,

benzo[4,5]imidazole[1,2-a]pyrimidines fluorescent compounds were screened and T557

was a hit for PHF tau binding (Zhang et al., 2012). When tested against brain slices with

tau pathology and Aβ plaques, T557 selectively bound to tau over Aβ. However, this

compound demonstrated poor brain uptake; therefore, T808 was developed through

optimization (Zhang et al., 2012). Autoradiography on 33 AD brain tissues (pathologically

confirmed diagnosis) and 12 non-AD healthy control cases was performed with [18F]T808.

Strong signals were observed in the “tau-rich/Aβ-rich” regions of the AD brains, but not

in the “tau-poor/Aβ-rich” regions of the AD brains, nor the “tau-poor/Aβ-poor” tissues in

of the non-AD cases (Zhang et al., 2012). This demonstrated selectivity of [18F]T808 for

tau over Aβ. [18F]T808 has an appropriate binding affinity (Kd=22nM) to PHF tau and its

24

pharmacokinetics are ideal for crossing the BBB. Testing in mouse models revealed fast

uptake and washout from the brain. When tested against 72 of the most common CNS

targets, [18F]T808 showed no inhibition at clinical concentrations, though there was mild

inhibition of the norepinephrine and monoamine transporter (Zhang et al., 2012).

The first human testing of [18F]T808 was performed in 8 AD patients and 3 healthy

controls (Chien et al., 2014). There was rapid uptake and distribution throughout the brain.

SUVRs in reference to the cerebellum were calculated for each VOI. These VOIs included

the frontal, parietal, lateral temporal, mesial temporal, and occipital lobes, the cerebellum,

the genu region of the white matter, and the approximate area of the hippocampus (Chien

et al., 2014). Due to the fast washout of the radiotracer from the cerebellum, SUVRs were

analyzed at both 30-50 minutes and 80-100 minutes. While the values from 80-100

minutes static frames were higher, the trends across the participants were similar for both

time frames. In all healthy control cases, SUVRs of the cortical regions were low. Mild

AD subjects had mild to moderate retention of [18F]T808 in the hippocampal area, mesial

temporal, lateral temporal, and parietal lobes. In the 80-100 minute time frame there was

also a moderate increase in frontal lobe retention. The SUVRs in the parietal, lateral

temporal, and frontal lobes, and hippocampal areas were increased in the moderate to

severe AD patients (Chien et al., 2014). These distribution patterns are reflective of PHF-

tau accumulation in AD (Braak et al., 1993; Chien et al., 2014).

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1.3.2.5 [18F]AV-1451

1.3.2.5.1 Development & Preclinical Testing

Another benzo[4,5]imidazole[1,2-a]pyrimidine-derived PET radioligand is [18F]AV-1451

([18F]T807). The first preclinical testing of [18F]AV-1451 was published in 2013 by Xia

and colleagues. Screening of fluorescent compounds against PHF tau in AD brains yielded

T726, which demonstrated selective binding to tau over Aβ plaques. Further optimization

of T726 produced the non-fluorescent analog T807 (AV-1451) (Xia et al., 2013).

Autoradiography was performed on frontal brain sections of 8 PHF-tau/Aβ rich slices, 9

PHF-tau poor/Aβ rich slices, and 9 PHF-tau/Aβ poor slices in conjunction with

immunohistochemistry staining to determine [18F]AV-1451 binding specificity.

Significant signal was only seen in the PHF-tau rich slices, indicating a >25 fold selective

binding of [18F]AV-1451 to tau over Aβ plaques (Xia et al., 2013). Pharmacokinetic

evaluation revealed promising properties to cross the BBB. [18F]AV-1451 has a low

molecular weight (262.1g/mol), is sufficiently lipophilic (logPoct of 3.4, logP of 1.67), and

possesses a high specific radioactivity of 9.36 Ci/µmol. Competitive binding assays with

72 of the most common CNS targets revealed minimal non-specific binding, with the

exception of the norepinephrine transporter and monoamine transporter. When tested in 6

mice, [18F]AV-1451 demonstrated rapid uptake into the brain and fast washout with some

retention in the bone, thought to be due to defluorination. The majority of [18F]AV-1451

was distributed to the kidneys and bladder, although there was a smaller portion delivered

26

to the liver. While four metabolites of the parent compound were noted, there were no

detectable metabolites in the brain (Xia et al., 2013). Overall preclinical evaluation of this

radiotracer yielded promising results that [18F]AV-1451 would effectively bind PHF-tau in

human brains.

A post-mortem study by Marquie and colleagues (2015) performed further

autoradiography screenings and included brain slices from an array of tauopathies. Brain

slices from 3 AD, 3 PiD, 3 PSP, 2 CBD, and 2 control cases were tested. A phosphor

screen [18F]AV-1451 autoradiography indicated strong signals in the entorhinal, frontal,

temporal, parietal, and occipital cortices of AD brain slices with tau positive NFTs. Non-

radiolabeled AV-1451 was able to competitively inhibited the [18F]AV-1451 signals

(Marquie et al., 2015). As expected, there was no signal in the control brain slices, with

the exception of the substantia nigra, which was confirmed to be neuromelanin containing-

cells. Non-PHF tauopathies (PiD, PSP, CBD, and transgenic mouse model) exhibited no

signal of [18F]AV-1451 in brain regions afflicted with their respective tau pathology.

However, in the PSP cases there was a signal in the entorhinal cortex, suggesting that

[18F]AV-1451 was labeling age-related tau accumulation according the Braak Staging

Theory (Braak et al., 1993; Marquie et al., 2015). Nuclear emulsion [18F]AV-1451

autoradiography results were also consistent with these findings. Silver grains

accumulated around both intracellular and extracellular PHF-tau in brain slices of AD, but

did not accumulate to any significant degree around Pick bodies of a PiD case, tufted

astrocytes in PSP, nor coiled bodies of CBD brain slices. It was concluded from this

preclinical study that [18F]AV-1451 bound with selectivity to PHF-tau in AD and age-

27

related tau accumulation, but did not bind to any significant degree to straight filaments of

tau that are present in non-AD tauopathies (PiD, PSP, and CBD), and aggregates

containing β-amyloid and α-synuclein. Additional binding to neuromelanin-containing

cells, such as those in the substantia nigra pars compacta was observed (Marquie et al.,

2015).

Another post-mortem examination of [18F]AV-1451 in AD (n=5), PSP (n=6), PiD (n=5),

and CBD (n=4) was performed by Sander and colleagues (2016). [18F]AV-1451 signal

was consistent with immunohistochemistry staining of tau in AD and PiD slices.

However, there was no substantial signal in PSP and CBD brain slices, consistent with the

previous post-mortem study (Sander et al., 2016).

1.3.2.5.2 Human Testing

[18F]AV-1451 was first tested clinically in 2 AD, 1 MCI, and 3 healthy controls (Chien et

al., 2013). The MMSE was used to test for cognitive ability in all participants. All 3

healthy controls scored 28 and above, the MCI patient had a score of 26, and the AD

patients were classified as mild and severe based on their scores of 21 and 7, respectively.

The MCI and AD patients underwent a [18F]Florbetapir (Amyvid®) PET scan to establish

the Aβ positive pathology (Chien et al., 2013). All participants underwent a dynamic PET

scan from 0-60min following the injection of [18F]AV-1451 and a subsequent 80-100min

static scan was also acquired. The mean activity injected for all participants was

28

10.11mCi, and peak activity was reached at 4-10min post injection in all areas of the

brain. VOIs were drawn manually on the CT scan acquired on the PET/CT scanner prior

to the PET scan. VOIs included the frontal, parietal, lateral temporal, mesial temporal,

and occipital lobes, the cerebellum, the genu region of the white matter, and the

approximate area of the hippocampus (Chien et al., 2013). Mean SUVs and SUVRs in

reference to the cerebellum were calculated for each VOI using the 80-100min static scan.

In the MCI patient SUVR was increased in the hippocampal area, and the parietal, mesial

temporal, and lateral temporal lobes. The mild AD patient had the highest SUVR in the

lateral temporal lobe, and additionally had high SUVRs in the mesial temporal, parietal,

frontal and occipital lobes, and the hippocampal area. The VOIs with the most elevated

SUVRs in the severe AD patient were the parietal and lateral temporal lobes, followed by

the mesial temporal, frontal, and occipital lobes, and the hippocampal area (Chien et al.,

2013). The severe AD patient had a significantly higher SUVR in the parietal lobe

compared to the mild AD and the MCI patients. The pattern of [18F]AV-1451 uptake in

the severe AD patient reflected the pathological tau deposition outline in Braak stages V-

VI (Braak et al., 1993; Chien et al., 2013). The mild AD patient had significantly higher

SUVR in the lateral temporal, mesial temporal lobes, and the hippocampal area compared

to the MCI patient and healthy controls. This retention distribution is characterized by

Braak stages III-IV (Braak et al., 1993; Chien et al., 2013). Interestingly, the oldest

healthy control (67 years old) had a higher SUV in the cerebellum, as well as the cortical

VOIs compared to the two younger healthy controls (56 and 58 years old). This is thought

to be due a an age-related accumulation of PHF tau (Chien et al., 2013).

29

Figure 1-2. SUVR images from static scan (80-100 minutes). Little to no retention is seen

in the healthy control. Mild retention in MCI and moderate retention in mild AD, with

increased cortical retention is seen in the severe AD patient. Reproduced with permission

from (Chien et al., 2013).

30

Figure 1-3. VOI SUVRs (80-100 minutes) for each subject. Cortical uptake differs from

AD to MCI and HC. Reproduced with permission from (Chien et al., 2013).

31

Another human study was performed with AD patients (44), MCI patients (87) and

healthy older adults (42) to test whether [18F]AV-1451 binding reflected the Braak

histopathological staging (Braak et al., 1993; Schwarz et al., 2016). The older control

group was defined as individuals ≥ 50 years old. Distribution of [18F]AV-1451 SUVR (80-

100 min) in all groups was in agreement with Braak staging patterns and previous

neuropathological studies. Of all the subjects, only 7 presented with signals that did not

conform to the neuropathological staging of tau deposition (Schwarz et al., 2016).

The post-mortem study performed by Marquie and colleagues (2015) prompted a human

study that aimed to further investigate the off-target binding of [18F]AV-1451 to

neuromelanin-containing cells in the substantia nigra (Hansen et al., 2016). PD is

neuropathologically marked by the depigmentation of the substantia nigra due to

denervation of dopaminergic neurons. PD patients (17) and healthy controls (16)

underwent a [18F]AV-1451 PET scan. There was a 30% decrease in [18F]AV-1451

midbrain signal in PD patients compared to control, which is consistent with the post-

mortem literature that a 30% reduction of dopaminergic neurons in the substantia nigra

pars compacta occurs before symptom presentation in PD. Additionally, there was no

signal in the nigra of pig and rat brains, because the nigra of non-primate species do not

contain neuromelanin (Hansen et al., 2016). [18F]AV-1451 is the first PET radiotracer to

bind neuromelanin and has the potential to measure depigmentation of the substantia nigra

in vivo in PD and other parkinsonian syndromes.

32

1.3.2.5.3 Significance

PSP is difficult to diagnose clinically, especially in the early stages due to its clinical

heterogeneity. Symptom overlap with other parkinsonian disorders, namely PD, and

tauopathies contribute to the misdiagnosis of PSP. A misdiagnosis could lead to an

inaccurate prognosis and prescription of inappropriate medication. [18F]AV-1451 could

serve as a new biomarker for the detection of PSP. Additionally, PET is an excellent tool

for monitoring disease progression and severity. New treatment options that decrease tau

burden can be assessed using the PET tracer to determine their molecular efficacy, and

reduce the large sample size and funding required by most clinical trials. There are many

other tauopathies that could also benefit from this radiotracer; therefore, validation of

[18F]AV-1451 in PSP patients offers diverse applications.

33

2.0 AIM & HYPOTHESIS

There are currently no PET radiotracers available to image pathological tau in PSP

patients in a clinical setting. While many tau radiotracers are still in the clinical trial

stages, most these studies have focused on AD, as this is the most common tauopathy.

PSP patients must be included in trials testing tau imaging agents in order to establish an

appropriate radiotracer for this patient population. Individuals with PSP could benefit

from the diagnostic and prognostic potential of a tau radiotracer due to the insidious and

aggressive nature of this neurodegenerative movement disorder. In vitro and in vivo

investigations using [18F]AV-1451 in AD and MCI patients have yielded promising

preliminary results, demonstrating its capability of to image tau pathology in these

disorders. However, further testing must be done to determine this radiotracer’s efficacy in

other tauopathies. AD has specific tau pathology (PHF tau) that differs from PSP and

other straight-filament tauopathies. Therefore, tau radiotracers that may effectively image

tau in AD are not necessarily useful for imaging tau pathology in PSP.

2.1 Aim

Determine whether [18F]AV-1451 is an appropriate PET radiotracer for imaging

pathological tau in PSP using PET and MRI technology.

34

2.2 Hypothesis

[18F]AV-1451 retention will be significantly elevated in the cortical and subcortical brain

regions of PSP patients, compared to PD and healthy controls. Specifically, elevation in

brain regions including the substantia nigra, caudate, putamen, globus pallidus, dentate

nucleus, frontal lobe, and parietal lobe are of interest to differentiate PSP from PD and

HC.

35

3.0 METHODS

3.1 Participants and Experimental Design

Twenty-two participants were recruited for this study: 6 PSP patients, 6 PD patients, and

10 healthy age-matched controls. PSP diagnoses were confirmed using the National

Institute for Neurological Disorder and Society (NNDS-SPSP) criteria. PSP disease

severity was measured using the Progressive Supranuclear Palsy Rating Scale (PSPRS)

and the Unified Parkinson’s Disease Rating Scale Part III (UPDRS-III). Patients with PD

met the UK Parkinson’s Disease Society Brain Bank Criteria and were also assessed with

the UPDRS-III to measure motor symptom severity.

The Unified Parkinson’s Disease Rating Scale (UPDRS) was originally developed during

the 1980’s to assess the severity of motor and non-motor symptoms associated with PD. In

2001, the Movement Disorders Society (MDS) evaluated, critiqued, and changed aspects

of the scale, renaming it the MDS-UPDRS (Goetz et al., 2008). Specifically, the MDS-

UPDRS III (“motor examination”) contains 18 items, some divided into left, right, and

other distributions for a total of 33 scores. Motor aspects tested include: finger taps, hand

movement, pronation/supination, toe tapping, leg agility, arising from chair, gait, freezing

of gait, postural stability, posture, global spontaneity of movement, postural tremor of

hands, kinetic tremor of hands, rest tremor amplitude, and constancy of rest tremor (Goetz

et al., 2008). Detailed instructions have also been included in the scale to ensure uniform

36

administration. When tested on 877 PD patients with 69 raters the new MDS-UPDRS III

had high internal consistency (Goetz et al., 2008).

The UPDRS III was tested on 175 PSP patients and while scores correlated with Hoehn

and Yahr staging, there were many PSP clinical features missing from the scale (Cubo et

al., 2000). In order to quantitatively assess PSP severity, Golbe and Ohman-Strickland

(2007) developed the PSP Rating Scale (PSPRS). This is a 6 part scale: history, mentation,

bulbar, ocular motor, limb motor, and gait and midline. There are 28 items, scored either

0-2 or 0-4, depending on the item (Golbe & Ohman-Strickland, 2007). The PSPRS

demonstrated a general increase in scores over time as PSP patient disability increased.

This rating scale also had high reliability when used by a clinician (Golbe & Ohman-

Strickland, 2007).

The National Institute for Neurological Disorder and Society for PSP developed criteria

(NNDS-SPSP) for diagnosing possible, probable, and definite PSP (Litvan et al., 1996).

All diagnoses required the disease onset after 40 years of age, a gradual progression of the

disorder, and no evidence of another disorder that could explain clinical symptoms.

Possible PSP necessitates vertical gaze palsy, OR slowing of vertical saccades and

postural instability accompanied by falls. A possible PSP diagnosis using these criteria has

a sensitivity of 83% and specificity of 93%. Probable PSP requires vertical gaze palsy,

AND slowing of vertical saccades and postural instability accompanied by falls. The

probable PSP criteria results in a highly specific diagnoses (100%); however, it has a

relatively low sensitivity (83%). Probable PSP criteria are appropriate for diagnoses in

37

research studies when other disorders must be excluded. A definite PSP diagnosis can

only be obtained with a clinically probable or possible PSP diagnosis AND

histopathological evidence (Litvan et al., 1996).

The Montreal Cognitive Assessment (MoCA) was used to score the general cognitive

abilities of all participants. In order to assess subjects’ depression level two weeks prior

and including the day of the screening, the Beck Depression Inventory (BDI) was

administered to all those enrolled in the study. Exclusion criteria for all participants are as

follows: no other history of neurodegenerative disorder(s), no alcohol or drug

dependency/abuse, unable or unwilling to undergo PET and/or MRI scan. Patients on

parkinsonian medication were required to undergo a 12-hour withdraw prior to their PET

scan. There was no withdrawal from psychotropic drugs. Additionally, control subjects

must not have had any history of neurological or psychiatric disorders.

Written informed consent was obtained from all participants. This study was approved by

the Research Ethics Board at the Centre for Addiction and Mental Health, University of

Toronto. In general, participants underwent PET and MRI scans on separate visits to limit

stress and fatigue, with the exception of one HC and one PD patient who had the MRI and

PET scans on the same day. No subjects were excluded due to any structural findings on

the MRI.

38

3.2 Radiosynthesis of [18F]AV-1451

Clinical doses of [18F]AV-1451 were produced by Centre for Addiction and Mental Health

(Toronto, ON). [18F]fluoride was produced using the cyclotron on site. The labeling

precursor, T807 (7-(6-fluoropyridin-3-yl)-5H-pyridol[4,3-b]indole), was radiolabeled with

18F in the presence of K222, K2CO3 and DMSO at 130ºC for 10 minutes. The mixture was

then cooled to 50ºC. A water wash was performed to get rid of the DMSO, any excess

[18F]fluoride, and impurities. The reaction mixture was transferred to an HPLC loop and

placed onto a semipreparative column (X-select HSS T3, 250 x 10.00mm, 5µ). To purify

the crude reaction mixture, 18% aqueous ethanol with a pH of 2 (controlled by HCL) was

used at a 5mL/min flow rate. Ultraviolet (λ=254nm) and radiochemical detectors

monitored the eluent for the 22 minute retention period. The collected HPLC fraction

containing the major radiochemical product was diluted with 8.4% sodium bicarbonate for

injection and 20mL of sterile water for injection. The product was then washed with water

to remove any salts, CH3CN, and [18F]fluoride. It was then eluted with 1 mL of ethanol

and 10mL of 0.9% sodium chloride for injection. The final solution was comprised of

10% ethanol in 0.9% sodium chloride.

This method of [18F]AV-1451 radiosynthesis had been tested to yield 14% (uncorrected)

in an average 60 minutes (Shoup et al., 2013).

39

3.3 MRI Acquisition

A 3.0 T GE Discovery MR750 MRI system (General Electric, Milwaukee, WI) was used

to acquire whole-brain proton density-weighted and T1-weighted MRI scans. The MRI

system was equipped with a GE Standard 8-Channel head coil. Proton density-weighted

2D images (oblique plane, 84 slices; matrix of 256 x 192; 22cm FOV; 2.0cm slice

thickness; TE = Min Full; TR = 6000 ms; flip angle = 8°) and T1-weighted 2D images

(sagittal plane, 200 slices; matrix of 256 x 230; 24cm field of view (FOV); 0.9cm slice

thickness; inversion time (TI) = 650ms; echo time (TE) = 3000ms; repetition time (TR) =

6700ms; flip angle = 8°). Proton density-weighted MRIs were obtained to provide

anatomical reference for ROI delineation of the PET scans. T1 weighted MRIs were used

for spatial normalization of parametric PET images.

3.4 PET Acquisition

PET scans were acquired on a high resolution PET/CT Siemens-Biograph HiRez XVI

(Siemens Molecular Imaging Knoxville, TN, U.S.A.). Prior to the PET scan, a low dose

(0.2mSv) CT scan was performed and used for attenuation correction. In order to prevent

head movement during the PET scan, a thermoplastic facemask was custom-fitted to each

participant and attached to a head-fixation system (Tru-Scan Imaging, Annapolis). A bolus

40

of [18F]AV-1451 was injected into the antecubital vein via an intravenous line. Injected

amount of [18F]AV-1451 ranged from 3.94-5.63 mCi, with an average specific activity of

2678.69mCi/µmol (+/-2165.65 mCi/µmol) and chemical purity of a least 95%. Following

the injection, a 90-minute PET scan was acquired. Emission list mode data was then

rebinned into a series of 3D sinograms that were corrected for attenuation and scatter. The

PET acquisition from 0 to 90 minutes was binned into 28 frames (8 x 15s, 3 x 60s, 5 x

120s, 5 x 300s, 5 x 600s). Fourier rebinning was then applied to convert the 3D sinograms

to 2D sinograms (Defrise et al., 1999). 2D filter back projection was used to reconstruct

the 2D sinograms into image space. Images were reconstructed with a spatial resolution of

2 × 2 × 2mm (x × y × z).

3.5 Image Analysis

3.5.1 Regions of Interest Analysis

Analysis of brain PET images can be performed on a voxel-based method, or a region of

interest (ROI)-based method. Rusjan and colleagues (2006) developed an automated

program to delineate ROIs and extract time-activity curves (TACs) for these regions. The

subject’s MR image (T1-weighted or proton density weighted) is used to transform a brain

template previously fitted to standard brain atlases (Kabani, Collins, & Evans, 1998;

Talairach & Tournoux, 1988) for all subjects. The brain template contains a series of

41

predetermined ROIs and these ROIs are altered and refined based on the probability of

grey matter voxels in the individual’s MRI. The subject’s MRI is then coregistered to the

PET image and the refined ROIs are converted to PET space and applied to the

individual’s PET image (Rusjan et al., 2006). The extracted TACs following this ROI

delineation were very reliable and were not subject to the intra and inter-operator

variability of manual ROI delineation (Rusjan et al., 2006).

3.5.2 Semi-quantification of Radioligand Uptake

In order to determine radiotracer plasma concentration and the rate constant for entry into

the brain from the plasma, a full kinetic compartmental analysis must be performed. If

such data is not currently available for a radioligand, a method of semi-quantitatively

measuring uptake and retention in various areas of the brain is calculating the standard

uptake value (SUV). SUV over time in a target region is based on the relationship between

the concentration of radioactivity in the region (Cimg, kBq/mL), the injected dose into the

subject (ID, MBq), and the subject’s body weight (BW, Kg).

(1) SUV (t) = CPET (t)

ID/BW

To compare target regions, SUV can be normalized based on a reference region that does

not contain any imaging target and therefore, any radiotracer binding in this region

42

represents nonspecific or off-target binding. Normalization using the SUV of a reference

region is called the standard uptake value ratio (SUVR).

(2) SUVR (t) = SUVtarget

SUVreference

3.5.3 [18F]AV-1451 Analysis

Standard uptake value ratios (SUVRs) were calculated in reference to the cerebellum and

corpus callosum for each ROI. ROIs were delineated using ROMI software, an automated

program designed by Rusjan and colleagues (2006) based on standardized atlases (Kabani

et al., 1998; Talairach & Tournoux, 1988). Using Statistical Parametric Mapping software

(SPM8, Welcome Department of Imaging Neuroscience, London, UK) each individual’s

MRI (GE 3T, proton density weighted, 1mm slice thickness) was used to non-linearly

transform a standardized brain template (International Consortium for Brain

Mapping/Montreal Neurological Institute 152 MRI) with predefined cortical and

subcortical ROIs. The individual ROI template was then further refined using SPM8 based

on grey matter probability of the segmented MRI. The refined individual ROIs were

aligned and resliced using a normalized mutual information algorithm to match the

individual’s PET scan. Time-activity curves (TACs) were then extracted for each ROI.

ROIs used for analysis were chosen based on brain regions implicated with tau pathology

in PSP post mortem studies; these included the substantia nigra, striatum, caudate,

43

putamen, globus pallidus, and thalamus (Williams et al., 2007). All cortical regions

(frontal, temporal, parietal, and occipital) were also included in the analysis. The ROI

template used in ROMI did not contain the dentate nucleus; therefore, this ROI was draw

manually and TACs were extracted using MarsBaR toolbox (Brett, Anton, Valabregue, &

Poline, 2002). SUVs for each ROI were calculated using the TAC values. SUVR for each

region was determined in reference to the cerebellum and corpus callosum, regions

considered free of pathological tau in PSP (Williams et al., 2007). Based on visual

inspection of the TACs, the 30 to 60 minute time point represented the pseudo-equilibrium

in which the radiotracer was both bound and unbound to the tau target; therefore, SUVR

values were averaged from 30 to 60 minutes post injection. Partial volume effect

correction was performed in order to control for differences in radiotracer uptake due to

atrophy in the patient groups (Rousset, Ma, & Evans, 1998).

Parametric PET images were created for each group by calculating mean SUVR (30-60

minutes) in SPM12 (Welcome Department of Imaging Neuroscience, London, UK) for

visualization purposes.

3.6 Statistical Analysis

[18F]AV-1451 SUV and SUVR were compared across groups using an independent

samples analysis of variance (ANOVA) and Bonferroni post hoc testing in IMB SPSS

Statistics 20 to measure significant differences in uptake between PSP, PD, and HC.

44

Linear regression was used to test whether age and MoCA scores were predictors of

[18F]AV-1451 uptake across the ROIs. Demographic factors were also analyzed to test for

differences between groups. An ANOVA and Bonferroni post hoc testing were performed

on age, MoCA scores, and BDI across all three participant groups. An independent

samples t-test was performed on disease duration and UPDRS to test for differences

between the PD and PSP patient groups. A threshold of p<0.05 was implemented to

determine significance.

45

4.0 RESULTS

4.1 Participant Demographics

Participant demographics are shown in table 4-1. There were 6 PSP patients, 6 PD

patients, and 10 HC participants enrolled in this study. Although PSP patients were, on

average, older than PD and HC subjects, there were no significant differences in age

between groups. Gender ratio across all three groups also did not differ significantly. As

expected, the mean MoCA scores of PSP patients were significantly lower than the mean

MoCA scores of PD patients and HC (p<0.001). All PSP MoCA scores were below 26,

indicating a certain level of cognitive decline. These results are consistent with the

previously reported cognitive impairment in PSP and the association of tau with dementia.

The mean BDI score of the PSP group was significantly greater than that of the PD group

(p<0.05) and HC group (p<0.001). PD patients had, on average a higher BDI than HC

(p<0.001). Elevated BDI scores in the patient groups were expected due to their physically

and mentally debilitating disorders. The mean UPDRS-III score (motor examination) of

PSP patients was significantly increased compared to PD patients (p<0.001), indicating

that PSP patients enrolled in this study tended to have more severe motor symptoms than

that of PD patients. Disease duration of PSP patients was, on average, shorter than PD

disease duration; however, this difference is not significant. These scores are consistent

with the aggressive nature of PSP onset. There were no significant differences in amount

of [18F]AV-1451 injected across the three groups.

46

Table 4-1. Participant demographics. Mean values (standard deviation).

a PSP is significantly different from PD at p<0.001. b PSP is significantly different from HC at p<0.001. c PSP is significantly different from PD at p<0.05. d PD is significantly different from HC at p<0.001.

PSP

(n=6)

PD

(n=6)

HC

(n=10)

Age in years

(+/-)

72.2 (6.77) 63.67 (9.61) 65.9 (9.93)

Gender

(M/F)

2/4 3/3 2/8

MoCA score

(+/-)

20.8 (2.39) a, b 28.3 (.715) 26.6 (1.51)

BDI score

(+/-)

14.8 (2.32) b, c 11.2 (2.23) d 2.56 (2.30)

UPDRS-III score

(+/-)

60.7 (7.58) a 26.3 (3.01) -

PSPRS score

(+/-)

45.0 (9.81) - -

Disease duration in

years (+/-)

4.00 (1.41) 5.50 (2.43) -

Amount injected in

mCi (+/-)

4.86 (.263) 4.72 (.261) 4.95 (.458)

47

4.2 ROI Analysis 4.2.1 Time-Activity Curves

[18F]AV-1451 uptake was rapid, reaching a peak between 5 to 12 minutes for most ROIs.

By the end of the 90-minute scan the cerebellum and cortical area activities were below 50

nCi/cc, whereas some of the subcortical region activity remained higher. Examples of the

time activity curves are present in figures 4-1 to 4-3.

48

Figure 4-1. Time-activity curve of cerebellum and putamen in PSP subject

-50

0

50

100

150

200

250

300

350

0 1000 2000 3000 4000 5000 6000

Act

ivit

y (

nC

i/cc

)

Time (s)

PSP Time-Activity Curve

Cerebellum

Putamen

49

Figure 4-2. Time-activity curve of cerebellum and putamen in PD subject.

-50

0

50

100

150

200

250

300

0 1000 2000 3000 4000 5000 6000

Act

ivit

y (

nC

i/cc

)

Time (s)

PD Time-Activity Curve

Cerebellum

Putamen

50

Figure 4-3. Time-activity curve of cerebellum and putamen in HC subject.

-50

0

50

100

150

200

250

0 1000 2000 3000 4000 5000 6000

Act

ivit

y (

nC

i/cc

)

Time (s)

HC Time-Activity Curve

Cerebellum

Putamen

51

4.2.2 SUV

Mean SUV values (30-60 minutes) for each ROI across the three groups are presented in

table 4-2. The highest SUVs were found in the subcortical regions across all participant

groups, specifically the striatum, caudate, and putamen. The highest SUV was in the

putamen for PSP (1.93), PD (1.73), and HC (1.90). In PSP, all subcortical regions had

elevated SUVs compared to cortical region SUVs. As mentioned the putamen had the

highest SUV (1.93), followed by the striatum (1.86), the globus pallidus (1.81), the

caudate (1.75), and the dentate nucleus (1.71). The ROIs with the most elevated SUVs in

PD included the putamen (1.73), the striatum (1.72), and the caudate (1.72). HC had the

highest SUVs in the putamen (1.90), the striatum (1.89), the caudate (1.96), and the

substantia nigra (1.75). Cortical SUVs in PSP ranged from 1.30-1.53, in PD these values

ranged from 1.31-1.38, and in HC 1.37-1.44. The lowest SUV in PSP was the frontal lobe

(1.30). In the PD group the lowest SUVs were found in the frontal lobe (1.31), the inferior

parietal lobe (1.32), and the occipital lobe (1.32). Similarly, the ROIs with the lowest

SUVs in HC were the occipital lobe (1.37) inferior parietal lobe (1.40), and the frontal

lobe (1.42). In every ROI, PD had the smallest SUV compared to PSP and HC. For the

HC and PSP, instead, the group with the highest SUV varied across the ROIs.

The SUVs of the reference regions (e.g. the cerebellum and corpus callosum), are

presented graphically in figures 4-4 and 4-6. Results for the cerebellar SUVs indicated that

PSP had the most uptake (1.46), followed by HC (1.38), and PD (1.24). Although there

are differences in SUV in this reference region across the three participant groups, these

52

variances are not significantly different (p>0.05). Within groups cerebellar SUVs there is

also variance. PSP SUVs range from 0.866 to 1.86, PD range is from 0.898 to 1.72 and

HC from 0.824 to 2.08. SUVs of the other reference region, the corpus callosum, also

differed across PSP (1.14), PD (0.978), and HC (1.08), though not significantly (p>0.05).

Within groups, the range of corpus callosum SUVs was slightly narrower than that of the

cerebellar SUVs. PSP ranged from 0.761 to1.47, PD from 0.659 to 1.34, and HC from

0.54 to 1.54.

SUVs were also calculated from 60-90 minutes post-injection. Values are presented in

table 4-3 and figures 4-5 and 4-7. Due to washout, the values are smaller from 60-90

minutes compared to the 30-60 minute time frame. Neither time frame demonstrated any

significant differences when comparing ROI SUVs across the three participant groups.

53

PSP PD HC

Frontal lobe

(+/-)

1.30 (.260)

1.31 (.343) 1.42 (.321)

Inferior parietal

lobe (+/-)

1.43 (.322)

1.32 (.336) 1.40 (.293)

Temporal lobe

(+/-)

1.48 (.314)

1.38 (.326) 1.44 (.297)

Occipital lobe

(+/-)

1.53 (.299)

1.32 (.235) 1.37 (.253)

Striatum

(+/-)

1.86 (.381)

1.72 (.432) 1.89 (.463)

Caudate

(+/-)

1.75 (.377)

1.72 (.453) 1.86 (.486)

Putamen

(+/-)

1.93 (.434)

1.73 (.431) 1.90 (.453)

Globus pallidus

(+/-)

1.81 (.543)

1.52 (.442) 1.66 (.398)

Substantia nigra

(+/-)

1.54 (.292)

1.31 (.354) 1.75 (.385)

Thalamus

(+/-)

1.67 (.343)

1.51 (.345) 1.69 (.351)

Dentate nucleus

(+/-)

1.71 (.314)

1.41 (.321) 1.58 (.364)

Cerebellum

(+/-)

1.46 (.328)

1.24 (.294) 1.38 (.330)

Corpus callosum

(+/-)

1.14 (.240)

.978 (.238) 1.08 (.229)

Table 4-2. Mean SUV (standard deviation) by ROI across groups from 30-60 minutes.

No significant differences between groups.

54

PSP PD HC

Frontal lobe

(+/-)

.817 (.151)

.819 (.242) .861 (.204)

Inferior parietal

lobe (+/-)

.927 (.223)

.827 (.243) .861 (.184)

Temporal lobe

(+/-)

.955 (.209)

.852 (.233) .888 (.189)

Occipital lobe

(+/-)

1.07 (.223)

.879 (.198) .890 (.166)

Striatum

(+/-)

1.17 (.262)

1.05 (.322) 1.14 (.308)

Caudate

(+/-)

1.07 (.327)

1.06 (.380) 1.09 (.319)

Putamen

(+/-)

1.24 (.304)

1.05 (.300) 1.16 (.304)

Globus pallidus

(+/-)

1.32 (.356)

1.01 (.323) 1.12 (.287)

Substantia nigra

(+/-)

1.14 (.338)

.916 (.333) 1.26 (.350)

Thalamus

(+/-)

1.07 (.245)

.869 (.239) .983 (.227)

Dentate nucleus

(+/-)

1.07 (.224)

.894 (.176) .997 (.288)

Cerebellum

(+/-)

.911 (.227)

.753 (.213) .813 (.223)

Corpus callosum

(+/-)

.918 (.203)

.768 (.206) .850 (.192)

Table 4-3. Mean SUV (standard deviation) by ROI across groups from 60-90 minutes.

No significant differences between groups.

55

Figure 4-4. Mean SUV of the cerebellum from 30-60 minutes.

Figure 4-5. Mean SUV of the cerebellum from 60-90 minutes.

56

Figure 4-6. Mean SUV of the corpus callosum from 30-60 minutes.

Figure 4-7. Mean SUV of the corpus callosum from 60-90 minutes.

57

4.2.3 SUVR Cerebellum

Mean SUVR values (30-60 minutes) were calculated using the cerebellum as a reference

region. Parametric images (figures 4-8, 4-10, 4-12) were created for each group to visually

depict the uptake in PSP, PD, and HC using SUVR. The subcortical regions, primarily the

striatal area, have the strongest signals across all three groups. In PSP, there was a notable

amount of atrophy. This atrophy was confirmed by visual inspection of the individual

MRIs. It appeared as though there was some uptake in the temporal and occipital lobes in

PSP, PD, and HC. Frontal lobe signal can be seen in PD and HC; however, there is little

signal in PSP. This may be due to frontal lobe atrophy that was seen on the individual PSP

MRIs. Partial volume correction was performed to address the possible effect of frontal

and ventricular atrophy on the uptake in PSP patients.

ROI analysis of SUVRs is presented across groups in table 4-4. SUVRs for PSP ranged

from 0.896 to 1.33 (frontal lobe and putamen, respectively). As with the SUVs, the

subcortical regions: putamen (1.33), striatum (1.29), caudate (1.25), and globus pallidus

(1.22), had the greatest SUVRs in PSP. The frontal and inferior parietal lobes (0.985)

were the only two regions in the PSP group that have SUVRs less than 1, indicating less

binding than the cerebellar reference region. No regions in PD and HC had SUVRs less

than 1. SUVRs for PD ranged from 1.06 (frontal lobe) to 1.40 (putamen). The putamen,

striatum (1.39), and caudate (1.39) presented with the highest SUVRs. Regions with the

lowest SUVR in PD included the frontal lobe (1.06), inferior parietal lobe (1.07), and the

substantia nigra (1.07). The frontal and inferior parietal lobes in the HC group also had the

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lowest SUVRs (1.03). The highest SUVR in HC was the putamen (1.39), followed by the

striatum (1.38) and the substantia nigra (1.33).

There were no ROIs that had a significantly higher SUVR in PSP patients compared to PD

and HC. The frontal lobe SUVR in PD (1.06) and HC (1.03) groups were significantly

higher than the frontal SUVR in PSP (0.896) (p<0.05). The substantia nigra SUVR in the

HC group (1.33) was also significantly increased compared to PSP (1.09) and PD (1.07)

(p<0.05). In the majority of ROIs, PD SUVRs are elevated (though not significantly)

compared to PSP, with the exception of the globus pallidus, the substantia nigra, and the

dentate nucleus. Similarly, HC SUVRs were higher than PSP SUVRs in all regions,

except the occipital lobe and the dentate nucleus.

Cerebellar SUVRs were also calculated from the 60-90 minute time point and presented in

table 4-5 and figures 4-9, 4-11, and 4-13. Almost all SUVRs were increased compared to

the 30-60 minutes SUVRs. This is due to the lower cerebellar SUVs. The only change in

significant differences across the three groups was that HC substantia nigra SUVR was not

significantly elevated compared to PSP, but remained significantly elevated compared to

PD.

59

PSP PD HC

Frontal lobe

(+/-)

.896 (.0707)

1.06 (.0685) a 1.03 (.0701) a

Inferior parietal

lobe

(+/-)

.985 (.0671)

1.07 (0.553) 1.03 (.0736)

Temporal lobe

(+/-)

1.03 (.0544)

1.12 (.0707) 1.06 (.0610)

Occipital lobe

(+/-)

1.07 (.0934)

1.09 (.0784) 1.02 (.0845)

Striatum

(+/-)

1.29 (.112)

1.39 (0.737) 1.38 (.126)

Caudate

(+/-)

1.22 (.213)

1.39 (0.672) 1.35 (.143)

Putamen

(+/-)

1.33 (.0747)

1.40 (.0857) 1.39 (.126)

Globus pallidus

(+/-)

1.25 (.206)

1.24 (.152) 1.26 (.217)

Substantia nigra

(+/-)

1.09 (.146)

1.07 (.114) 1.33 (.161) a,b

Thalamus

(+/-)

1.16 (.120)

1.22 (.0945) 1.24 (.0611)

Dentate nucleus

(+/-)

1.19 (.143)

1.16 (.0395) 1.16 (.115)

Cerebellum

(+/-)

-

- -

Table 4-4. Mean SUVR (standard deviation) from 30-60 minutes using the cerebellum as

a reference region. ANOVA and post hoc (Bonferroni) results.

a SUVR was significantly elevated compared to PSP SUVR at p<0.05 b SUVR was significantly elevated compared to PD SUVR at p<0.05

60

PSP PD HC

Frontal lobe

(+/-)

.912 (.0921)

1.09 (.101) a 1.07 (.121) a

Inferior parietal

lobe (+/-)

1.02 (.0583)

1.10 (.0817) 1.08 (.108)

Temporal lobe

(+/-)

1.06 (.0500)

1.14 (.0988) 1.11 (.0979)

Occipital lobe

(+/-)

1.19 (.0992)

1.19 (.135) 1.12 (.115)

Striatum

(+/-)

1.30 (.213)

1.38 (.0830) 1.40 (.182)

Caudate

(+/-)

1.21 (.383)

1.39 (.141) 1.34 (.194)

Putamen

(+/-)

1.36 (.142)

1.39 (.0594) 1.43 (.184)

Globus pallidus

(+/-)

1.44 (.274)

1.33 (.155) 1.39 (.174)

Substantia nigra

(+/-)

1.27 (.271)

1.21 (.258) 1.57 (.235) b

Thalamus

(+/-)

1.18 (.0750)

1.15 (.0984) 1.22 (.0945)

Dentate nucleus

(+/-)

1.19 (.113)

1.21 (.122) 1.22 (.180)

Cerebellum

(+/-)

-

- -

Table 4-5. Mean SUVR (standard deviation) from 60-90 minutes using the cerebellum as

a reference region. ANOVA and post hoc (Bonferroni) results.

a SUVR was significantly elevated compared to PSP SUVR at p<0.05 b SUVR was significantly elevated compared to PD SUVR at p<0.05

61

Figure 4-8. Mean PSP group parametric image of mean SUVR 30-60 minutes.

Figure 4-9. Mean PSP group parametric image of mean SUVR 60-90 minutes.

62

Figure 4-10. Mean PD group parametric image of mean SUVR 30-60 minutes.

Figure 4-11. Mean PD group parametric image of mean SUVR 60-90 minutes.

63

Figure 4-12. Mean HC group parametric image of mean SUVR 30-60 minutes.

Figure 4-13. Mean HC group parametric image of mean SUVR 60-90 minutes.

64

Figure 4-14. Mean SUVRs of the frontal lobe from 30-60 minutes using the cerebellum as

a reference region.

Figure 4-15. Mean SUVRs of the frontal lobe from 60-90 minutes using the cerebellum as

a reference region.

65

Figure 4-16. Mean SUVRs of the inferior parietal lobe from 30-60 minutes using the

cerebellum as a reference region.

Figure 4-17. Mean SUVRs of the inferior parietal lobe from 60-90 minutes using the

cerebellum as a reference region.

66

Figure 4-18. Mean SUVRs of the temporal lobe from 30-60 minutes using the cerebellum

as a reference region.

Figure 4-19. Mean SUVRs of the temporal lobe from 60-90 minutes using the cerebellum

as a reference region.

67

Figure 4-20. Mean SUVRs of the occipital lobe from 30-60 minutes using the cerebellum

as a reference region.

Figure 4-21. Mean SUVRs of the occipital lobe from 60-90 minutes using the cerebellum

as a reference region.

68

Figure 4-22. Mean SUVRs of the caudate from 30-60 minutes using the cerebellum as a

reference region.

Figure 4-23. Mean SUVRs of the caudate from 60-90 minutes using the cerebellum as a

reference region.

69

Figure 4-24. Mean SUVRs of the putamen from 30-60 minutes using the cerebellum as a

reference region.

Figure 4-25. Mean SUVRs of the putamen from 60-90 minutes using the cerebellum as a

reference region.

70

Figure 4-26. Mean SUVRs of the striatum from 30-60 minutes using the cerebellum as a

reference region.

Figure 4-27. Mean SUVRs of the striatum from 60-90 minutes using the cerebellum as a

reference region.

71

Figure 4-28. Mean SUVRs of the globus pallidus from 30-60 minutes using the

cerebellum as a reference region.

Figure 4-29. Mean SUVRs of the globus pallidus from 60-90 minutes using the

cerebellum as a reference region.

72

Figure 4-30. Mean SUVRs of the substantia nigra from 30-60 minutes using the

cerebellum as a reference region.

Figure 4-31. Mean SUVRs of the substantia nigra from 60-90 minutes using the

cerebellum as a reference region.

73

Figure 4-32. Mean SUVRs of the thalamus from 30-60 minutes using the cerebellum as a

reference region.

Figure 4-33. Mean SUVRs of the thalamus from 60-90 minutes using the cerebellum as a

reference region.

74

Figure 4-34. Mean SUVRs of the dentate nucleus from 30-60 minutes using the

cerebellum as a reference region.

Figure 4-35. Mean SUVRs of the dentate nucleus from 60-90 minutes using the

cerebellum as a reference region.

75

4.2.4 Partial Volume Correction

Partial volume correction was performed in order to account for any effects on uptake due

to atrophy and small ROIs. Across all three groups and in every ROI the SUVR was

increased due to partial volume correction. Values from 30-60 minutes are presented in

table 4-6.

As expected, partial volume correction eliminated the significant differences between the

PSP frontal lobe SUVR and that of PD and HC. Partial volume correction also greatly

affected the caudate, increasing the PSP SUVR from 1.22 to 2.41, the PD SUVR from

1.39 to 2.41, and the HC SUVR from 1.35 to 2.32. The significant increase in the HC

SUVR of the substantia nigra compared to PSP and PD was removed. PSP had the highest

substantia nigra SUVR (2.61), followed by HC (2.34), and PD (1.83). Partial volume

correction did introduce one new significant different not seen in the uncorrected data.

The occipital SUVR in the PD group (1.64) was significantly higher than that of the HC

group (1.41).

As a general trend, partial volume correction reduced the difference in uptake between the

cortical regions and the subcortical regions. There was no longer the distinction of low

SUVR in the cortical areas and higher SUVR in the subcortical areas. With partial volume

correction, values varied from 1.32 to 2.61 in PSP, 1.34 to 2.41 in PD, and 1.24 to 2.34 in

HC. The majority of PD and HC ROI SUVRs were no longer elevated compared to PSP.

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The partial volume effect corrected SUVRs were also calculated from 60-90 minutes and

presented in table 4-7. With the exception of the thalamus, caudate, and putamen, all other

ROIs had higher SUVRs compared to the 30-60 SUVRs. The only significant difference

between groups remains in the occipital lobe.

PSP PD HC

Frontal lobe

(+/-)

1.66 (.124)

1.65 (.184) 1.52 (.178)

Parietal lobe

(+/-)

1.84 (.188)

2.00 (.352) 1.74 (.155)

Temporal lobe

(+/-)

1.32 (.0676)

1.34 (.126) 1.24 (.108)

Occipital lobe

(+/-)

1.57 (.174)

1.64 (.172) a 1.41 (0.101)

Caudate

(+/-)

2.41 (.508)

2.41 (.312) 2.32 (.603)

Putamen

(+/-)

1.33 (.0508)

1.48 (.121) 1.34 (.188)

Globus pallidus

(+/-)

1.47 (.153)

1.36 (.348) 1.52 (.135)

Substantia nigra

(+/-)

2.61 (.694)

1.83 (.427) 2.34 (.460)

Thalamus

(+/-)

1.97 (.150)

1.85 (.148) 1.83 (.166)

Table 4-6. Partial volume corrected mean SUVR (standard deviation) from 30-60 minutes

using the cerebellum as a reference region.

a SUVR was significantly elevated compared to HC at p<0.05.

77

PSP PD HC

Frontal lobe

(+/-)

1.68 (.215)

1.70 (.232) 1.51 (.238)

Parietal lobe

(+/-)

1.88 (.236)

2.04 (.402) 1.72 (.240)

Temporal lobe

(+/-)

1.36 (.0893)

1.38 (.178) 1.24 (.133)

Occipital lobe

(+/-)

1.75 (.211)

1.79 (.319) a 1.49 (0.132)

Caudate

(+/-)

2.17 (.590)

2.39 (.301) 2.13 (.583)

Putamen

(+/-)

1.31 (.215)

1.43 (.0723) 1.37 (.243)

Globus pallidus

(+/-)

1.71 (.203)

1.57 (.484) 1.47 (.278)

Substantia nigra

(+/-)

3.29 (1.48)

1.94 (.866) 2.59 (.597)

Thalamus

(+/-)

1.91 (.219)

1.72 (.191) 1.70 (.153)

Table 4-7. Partial volume corrected mean SUVR (standard deviation) from 60-90 minutes

using the cerebellum as a reference region.

a SUVR was significantly elevated compared to HC at p<0.05.

78

Figure 4-36. Mean partial volume corrected SUVRs of the frontal lobe from 30-60

minutes using the cerebellum as a reference region.

Figure 4-37. Mean partial volume corrected SUVRs of the frontal lobe from 60-90

minutes using the cerebellum as a reference region.

79

Figure 4-38. Mean partial volume corrected SUVRs of the parietal lobe from 30-60

minutes using the cerebellum as a reference region.

Figure 4-39. Mean partial volume corrected SUVRs of the parietal lobe from 60-90

minutes using the cerebellum as a reference region.

80

Figure 4-40. Mean partial volume corrected SUVRs of the temporal lobe from 30-60

minutes using the cerebellum as a reference region.

Figure 4-41. Mean partial volume corrected SUVRs of the temporal lobe from 60-90

minutes using the cerebellum as a reference region.

81

Figure 4-42. Mean partial volume corrected SUVRs of the occipital lobe from 30-60

minutes using the cerebellum as a reference region.

Figure 4-43. Mean partial volume corrected SUVRs of the occipital lobe from 60-90

minutes using the cerebellum as a reference region.

82

Figure 4-44. Mean partial volume corrected SUVRs of the caudate from 30-60 minutes

using the cerebellum as a reference region.

Figure 4-45. Mean partial volume corrected SUVRs of the caudate from 60-90 minutes

using the cerebellum as a reference region.

83

Figure 4-46. Mean partial volume corrected SUVRs of the putamen from 30-60 minutes

using the cerebellum as a reference region.

Figure 4-47. Mean partial volume corrected SUVRs of the putamen from 60-90 minutes

using the cerebellum as a reference region.

84

Figure 4-48. Mean partial volume corrected SUVRs of the globus pallidus from 30-60

minutes using the cerebellum as a reference region.

Figure 4-49. Mean partial volume corrected SUVRs of the globus pallidus from 60-90

minutes using the cerebellum as a reference region.

85

Figure 4-50. Mean partial volume corrected SUVRs of the substantia nigra from 30-60

minutes using the cerebellum as a reference region.

Figure 4-51. Mean partial volume corrected SUVRs of the substantia nigra from 60-90

minutes using the cerebellum as a reference region.

86

Figure 4-52. Mean partial volume corrected SUVRs of the thalamus from 30-60 minutes

using the cerebellum as a reference region.

Figure 4-53. Mean partial volume corrected SUVRs of the thalamus from 60-90 minutes

using the cerebellum as a reference region.

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4.2.5 SUVR Corpus Callosum

Mean SUVR values (30-60 minutes) were calculated using the corpus callosum as a

reference region and presented in table 4-8. The SUVRs using the corpus callosum were

higher than the SUVRs using the cerebellum as a reference region because the corpus

callosum SUVs were lower than that of the cerebellum. The highest SUVRs in the PSP

group included the striatum (1.63), the putamen (1.61), the globus pallidus (1.59), and the

caudate (1.56). In PD patients the putamen (1.75), the striatum (1.74, and the caudate

(1.74) had the highest SUVRs. The ROIs with the most elevated SUVR were the putamen

(1.74), the striatum (1.72), and the caudate (1.70) In no cases across the three groups was

the SUVR below 1, indicating that all ROIs had a greater uptake of [18F]AV-1451

compared to the corpus callosum.

PSP and PD did not differ significantly across any SUVRs. The HC group SUVR was

significantly elevated in the substantia nigra (1.62) compared to PSP (1.35) and PD (1.33)

(p<0.05).

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PSP PD HC

Frontal lobe

(+/-)

1.20 (.200)

1.33 (.0485) 1.30 (.153)

Inferior parietal

lobe

(+/-)

1.28 (.134)

1.34 (.0450) 1.29 (.149)

Temporal lobe

(+/-)

1.31 (.0887)

1.39 (.0560) 1.33 (.134)

Occipital lobe

(+/-)

1.30 (.0938)

1.36 (.0961) 1.27 (.161)

Striatum

(+/-)

1.63 (.143)

1.74 (.0628) 1.72 (.234)

Caudate

(+/-)

1.56 (.299)

1.74 (.0651) 1.70 (.266)

Putamen

(+/-)

1.61 (.237)

1.75 (.0798) 1.74 (.225)

Globus pallidus

(+/-)

1.59 (.269)

1.53 (.160) 1.52 (.149)

Substantia nigra

(+/-)

1.35 (.122)

1.33 (.103) 1.62 (.258) a,b

Thalamus

(+/-)

1.39 (.143)

1.53 (.0903) 1.55 (.147)

Dentate nucleus

(+/-)

1.47 (.114)

1.44 (.0787) 1.44 (.0883)

Cerebellum

(+/-)

-

- -

Table 4-8. Mean SUVR (standard deviation) from 30-60 minutes using the corpus

callosum as a reference region. a SUVR was significantly elevated compared to PSP SUVR at p<0.05 b SUVR was significantly elevated compared to PD SUVR at p<0.05

89

4.3 [18F]AV-1451 and MoCA

Due to the cognitive component that accompanies many tauopathies, MoCA scores were

tested against [18F]AV-1451 uptake across all subjects. Logistic regression results

indicated that MoCA scores were not predictors of SUVRs using the cerebellum as a

reference region (both partial volume effect corrected and uncorrected) in any of the ROIs

analyzed.

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5.0 DISCUSSION

5.1 Overview of Findings

This study was designed to contribute to the ever-growing research of tau in vivo imaging.

While tau is involved in many neurodegenerative disorders, PSP is one that has been

underrepresented in many tau neuroimaging studies. In order to advance the current

knowledge regarding tau and PSP, we investigated the PET radiotracer [18F]AV-1451 in

this patient population. It was predicted that [18F]AV-1451 uptake in cortical and

subcortical regions would be significantly greater in PSP patients compared to PD patients

and HC. Relationships between [18F]AV-1451 uptake and demographic information were

also explored to further test the efficacy of the radiotracer.

5.1.1 Demographics

Analysis of demographic characteristics revealed several significant differences between

groups. PSP patients had, on average, a lower MoCA scores than PD and HC participants,

suggesting decreased general cognition. All PSP patients had scores less than 26 on the

MoCA scale, indicating below average cognition and potentially cognitive impairment,

though further testing would have to be done for confirmation (Zadikoff et al., 2008).

Significantly lower MoCA scores are consistent with previous literature, connecting

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cognitive impairment with both tau pathology and as a clinical manifestation in some PSP

cases (Brown et al., 2010). Lower MoCA scores were anticipated to be correlated with

increased tau load, and therefore, increased [18F]AV-1451 uptake; however, MoCA scores

were not significant predictors of radiotracer retention.

Another significant demographic difference between PSP, PD, and HC were BDI scores.

The PSP group had a significantly higher mean BDI score compared to both PD and HC.

PD patients had, on average, higher BDI scores than HC scores. In fact, all PSP and PD

scores were above all HC scores. This indicates that PSP patients have a significantly

higher incidence of depressive symptoms, as measured by mood and activity patterns in

the two weeks prior to the screening date. Due to the degenerative nature of PSP and PD,

it is expected that self-esteem and self-worth are affected as motor and cognitive

symptoms worsen and activities of daily living become more challenging. Difficulty

adjusting to one’s current situation may be a factor that reduces the general mood of PSP

and PD patients and lead to increased BDI scores compared to HC subjects.

The final demographic characteristic that differed significantly between groups was the

UPDRS-III scores of PSP and PD patients. All PSP patients scored higher than the PD

patients, and the PSP group average UPDRS-III score was significantly elevated

compared to the PD average score, indicating increased motor symptom severity in PSP

patients. This finding is consistent with previous reports of PSP as an aggressive disease

with rapid onset of motor symptoms (Liscic et al., 2013). Another finding that supports

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the aggressive nature of PSP was that the mean disease duration of PSP was shorter than

that of PD; however, this difference was not statistically significant.

5.1.2 [18F]AV-1451 Retention

Using SUVR as a semi-quantitative method of measuring [18F]AV-1451 uptake in the

brains of PSP, PD, and HC subjects revealed some changes in a few ROIs, though not the

differences that were expected. SUVR was not significantly higher in PSP compared to

PD and HC in any of the ROIs analyzed. These results are consistent with the findings of

Marquie and colleagues (2015). [18F]AV-1451 phosphor screen autoradiography of the

frontal, parietal, temporal, and occipital cortices, hippocampus, entorhinal cortex,

cingulate, basal ganglia, midbrain, and the cerebellum was performed on three

pathologically confirmed PSP brains. Tau immunoreactivity was tested on adjacent brain

slices and compared to the phosphor screen results. Although the brain slices analyzes

displayed straight filaments comprised of tau inclusions, the phosphor screen results

displayed no [18F]AV-1451 signal in PSP brain regions with non-PHF-tau aggregations

(Marquie et al., 2015).

Interestingly, there were two ROIs in which PD and HC had a significantly greater

[18F]AV-1451 retention compared to the PSP group. The mean frontal lobe SUVR of PD

and HC using the cerebellum as a reference region was significantly higher than that of

PSP. However, this statistically significant difference was not present in the corpus

93

callosum SUVR analysis and is not consistent with pathological studies of PSP post

mortem brains; therefore, this finding may be due to one of the experimental limitations of

this study (Williams et al., 2007). In both cerebellar and corpus callosum analysis, the

mean HC substantia nigra SUVR was significantly higher than both PSP and PD groups.

Partial volume correction removed this difference (see section 5.1.4).

5.1.3 Reference Regions

Our results using both the cerebellum and the corpus callosum as reference regions

demonstrated higher SUVRs in the subcortical regions compared to cortical regions. The

corpus callosum had lower SUVs in all ROIs compared to the cerebellum; therefore,

SUVRs using the corpus callosum were all higher than SUVRs using the cerebellum as a

reference region. The reason for disparities among the reference regions could be due to

the fact that one is grey matter (cerebellum) and the other is white matter (corpus

callosum); therefore, the amount of non-specific binding differs. It is for this reason that

analysis was primarily performed using the cerebellum as a reference region because all

ROI analyzed were also grey matter. The corpus callosum analysis served as a comparison

of retention patterns, and was consistent with the cerebellar results. The mean PSP

cerebellar SUV was higher than that of PD and HC groups; however, the mean PSP

corpus callosum SUV was also elevated compared to PD and HC, and all three groups

demonstrated an increased cerebellar SUV compared to their respective corpus callosum

SUV. If the cerebellum or the corpus callosum contained any specific binding (i.e. tau

94

aggregates) then they would not be appropriate reference regions and the retention in PSP

patients would underrepresented by their SUVR calculation. This possibility is further

discussed in section 5.3 (experimental limitations) and section 7.0 (future directions).

5.1.4 Atrophy and Partial Volume Correction

Inspection of individual MRIs revealed considerable atrophy in the ventricles and frontal

lobes of PSP patients compared to PD and HC MRIs. This finding is consistent with

previous MRI studies involving PSP cases (Giordano et al., 2013; Massey et al., 2012).

Atrophy can reduce the size of ROIs, making it difficult to segment (the grey matter, white

matter, and CSF) and normalize the MRI, thereby risking the possibility of inaccurate ROI

delineation. Successful segmentation and normalization was achieved for all subjects and

the ROI template was carefully matched to each individual PET scan. Another concern

with smaller ROIs due to atrophy is the partial volume effect. This phenomenon causes

‘spill in’ and/or ‘spill out’ of measured radioactivity in any given ROI. ‘Spill out’ is of

particular interest in the cases of small ROIs with high activity because the measured

activity within the ROI ‘spills out’ into neighbouring areas, making the small ROI appear

to have less retention of the radiotracer than is in fact true. Due to the amount of atrophy

that was seen in the PSP MRIs we believed that partial volume correction should be

applied in order to control for the potential ‘spill out’ effect.

95

Partial volume effect correction was performed on all PET scans, regardless of the subject

group, to ensure objectivity and consistency between the three groups and also within

groups. As expected, partial volume effect correction increased the SUVRs of some PSP

ROIs. However, this correction also increased the SUVRs of the same PD and HC ROIs;

therefore, there were still no significant increases in PSP SUVR compared to PD and HC.

Partial volume effect correction did eliminate the distinction between low cortical SUVRs

and higher subcortical SUVRs. Increased frontal lobe SUVR, particularly in PSP was

predicted, as this region is involved in the later stages of tau pathology development, and

presented with substantial atrophy, thereby diminishing the relative activity measured in

the frontal lobe (Williams et al., 2007). Significant increases in caudal SUVRs were also

seen across all three groups. While there may have been atrophy in the caudate of PSP

patients, it is unlikely that there was the same amount of atrophy in all groups; therefore,

this increase in SUVR is not believed to be due to presence of tau pathology in PSP. The

substantia nigra was another ROI that demonstrated a substantial increase in SUVR

following partial volume effect correction. It is unclear how accurate [18F]AV-1451

binding is in this area due to previously reported data. [18F]AV-1451 phosphor screen

autoradiography revealed signals on tauopathy (PSP and AD) and non tauopathy (control

and dementia with Lewy bodies) substantia nigra brain slices (Marquie et al., 2015). This

may indicate nonspecific binding of [18F]AV-1451 in this brain area and signals in the

control brain slices is consistent with our findings in the non-partial volume effect

corrected data. Partial volume effect correction removed the significant increase of HC

substantia nigra SUVR compared to PSP and PD. Based on these results and previously

96

reported data it is unclear how reliable [18F]AV-1451 is at selectively imaging

pathological tau in the substantia nigra.

5.1.5 Off Target Binding

In previous post mortem and human studies, [18F]AV-1451 was found to bind melanin and

neuromelanin-containing cells (Hansen et al., 2016; Marquie et al., 2015). This study

demonstrated consistent results. [18F]AV-1451 SUVR in the substantia nigra of HC was

significantly higher than SUVR in PD and PSP from 30-60 minutes, and significantly

greater than PD from 60-90 minutes. While this significant difference did not stand PVC,

HC still tended to have a higher mean SUVR when compared to PD and PSP. These

results, along with results from in vitro and in vivo studies, suggest that [18F]AV-1451 is

capable of binding neuromelanin-containing cells in the substantia nigra and may be

useful for imaging the depigmentation of the substantia nigra that occurs in PD and other

parkinsonian disorders.

5.2 Tau Radiotracers for PSP

Tauopathies differ from each other in many ways: symptomology, tau load pattern, etc.

One of the most important characteristics to successfully image tau in vivo in all

tauopathies is the ability of an imaging agent to bind all conformations of tau. Tauopathies

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contain varying ratios of tau isoforms, the ratio in AD is similar to that of a healthy brain

with an equal 3R to 4R ratio, as indicated by its major tau bands of 60, 64 and 68 kDa and

minor band of 72 kDa illustrated in figure 5-1. PSP and CBD tau aggregates run as major

bands of 64 and 68 kDa and minor band at 72 kDa are primarily composed of 4R

isoforms, while Pick bodies in PiD are generally formed by 3R isoforms, as demonstrated

by the major tau bands of 60 and 64 kDa and a minor band of 68 kDa. An ideal radiotracer

for all tauopathies would be capable of imaging both isoforms. Previous in vitro and in

vivo work with [18F]AV-1451 on AD brain slices and AD patients suggests that this

radioligand is capable of binding both 3R and 4R tau isoforms (Chien et al., 2013;

Schwarz et al., 2016; Xia et al., 2013). Additionally when tested in post mortem tissue,

[18F]AV-1451 phosphor screen autoradiography demonstrated signal in AD but no signal

in PSP and CBD (4R), and PiD (3R). Therefore, [18F]AV-1451 does not seem to

preferentially image one tau isoform over the other. Lack of signal in PSP brain slices and

the absence of differences between our groups is not likely due to the 4R tau isoform

present in PSP tau inclusions.

Another differential between AD and other tauopathies is the structure of its tau

aggregates. AD tau inclusions form primarily PHF conformations, with only

approximately 5% of total tau load represented by straight filaments (Spillantini, Bird, &

Ghetti, 1998). PHFs have a diameter of approximately 8-20 nm and periodicity of 80 nm.

PSP and other tauopathies (CBD and PiD) present primarily with straight filaments and to

a much lesser degree PHFs. Differences between PHF tau and straight filaments is

illustrated in figure 5-2.

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Figure 5-1. Diagram of tau band variance across different tauopathies. Type I: major tau

bands of 60, 64 and 68 kDa and a minor tau band of 72 kDa. Type II: major tau bands of

60 and 64 kDa and a minor tau band of 68 kDa. Type III: major tau bands of 64 and 68

kDa and a minor tau band of 72 kDa. Reproduced with permission from (Spillantini et al.,

1998).

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Figure 5-2. Electron micrographs of tau filaments (scale bar: 100nm). A: PHF in AD. B:

straight filament in PSP. C: PHF in Down syndrome. D: straight filament in PiD. E: PHF

in Seattle family. F: twisted filament in familial MSTD. Reproduced with permission from

(Spillantini et al., 1998).

100

The findings of this study, and the previously published literature on [18F]AV-1451

suggest that this radiotracer may not be appropriate for imaging pathological tau in PSP.

[18F]AV-1451 was initially screened for tau inclusion in PHF conformations from AD

brains; therefore, the hits that were produced from these screenings were compounds that

bound PHF tau and not necessarily straight filaments of tau. The first testing in vitro

performed on AD brains slices and in vivo testing on MCI and AD patients indicated that

[18F]AV-1451 could bind PHF tau and differentiate AD from MCI and HC, thereby

initially designating [18F]AV-1451 as a promising radiotracer for imaging tau in vivo

(Chien et al., 2013; Xia et al., 2013). Further studies also demonstrated that [18F]AV-1451

was capable of binding tau in AD (Marquie et al., 2015; Schwarz et al., 2016). However,

when [18F]AV-1451 is applied to PSP cases this current clinical research supports the

previously published post mortem data that [18F]AV-1451 does not bind pathological tau

in PSP (Marquie et al., 2015). The most probable reason being that [18F]AV-1451 binds

PHF tau selectively over straight filaments of tau.

5.3 Experimental Limitations

There were limitations in this experimental design that may have affected the results and

prevented us from rejecting our null hypothesis. The first limitation is the small sample

size of our study. Due to the preliminary nature of this study, and the first of its kind

testing [18F]AV-1451 in both PSP and PD patients, the aim was to conduct this

investigation on a small sample as a pilot study. However, a small sample size reduces

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power and limits the ability of detecting differences between groups. There may have been

a true difference in [18F]AV-1451 retention in PSP compared to PD and HC; however,

with no previous knowledge of effect size it is difficult to confirm whether or not our

sample was large enough to detect such possible differences.

For this pilot study, we decided to analyze the uptake of [18F]AV-1451 using SUVR, a

semi-quantitative and less invasive method. A full kinetic analysis of [18F]AV-1451 has

yet to be published; therefore, rate constants and the ideal method of analyzing [18F]AV-

1451 data remain unknown. SUVR is an estimate of the uptake of [18F]AV-1451 and has

not been compared to arterial sampling. It is unclear whether or not SUVR is an

appropriate method of measuring [18F]AV-1451 retention in the brain. If a full kinetic

analysis of [18F]AV-1451were to reveal that SUVR does not accurately estimate the

radiotracer’s uptake, then this may call into question our results. In this case, [18F]AV-

1451 could potentially be an appropriate radiotracer for imaging tau in PSP; however,

SUVR is not able to detect differences in [18F]AV-1451 retention between groups.

Another potential limitation arises with the reference regions because SUVR relies heavily

on their accuracy and reliability. The cerebellum and corpus callosum were chosen as

reference regions for PSP because pathological studies have demonstrated no tau

aggregation in these areas (Williams et al., 2007). An ideal reference region represents

only nonspecific binding that is also equivalent to the nonspecific binding found in other

ROIs. If the reference region contains any specific binding or uptake in this region that is

not representative of the rest of the brain then it will alter the results for all ROIs.

102

Generally reference regions across all subjects should have similar uptake; however, our

results indicate a wide difference across subjects for both the cerebellum and the corpus

callosum. This may be indicative of variable blood flow or variable nonspecific binding in

these areas between subjects, or it may indicate that these are not suitable reference

regions. To confirm whether the cerebellar and corpus callosum are appropriate reference

regions to use when analyzing [18F]AV-1451 a full kinetic analysis would need to be

performed.

103

6.0 Conclusion

The aim of this study was to determine whether [18F]AV-1451 is an appropriate

radiotracer for imaging pathological tau in PSP patients. Retention of [18F]AV-1451 was

not significantly higher in PSP compared to PD and HC in any of the ROIs analyzed in

this study. We also tested whether general cognition predicted the uptake of [18F]AV-1451

because of the relationship between tau deposition and cognitive decline. There was no

significant relationship between MoCA scores and [18F]AV-1451 SUVR in any of the

ROIs. Based on our preliminary findings and previous literature regarding the different

conformations of tau pathology found in AD and PSP, [18F]AV-1451 may only bind to

PHF tau and not the straight filaments of tau found in PSP. If this is the case, then

[18F]AV-1451 is not a valid tau radiotracer for PSP and other radiotracers should be

investigated. It is also possible that the analysis method we used to measure the retention

of [18F]AV-1451 (SUVR) is an not an appropriate modality to detect differences between

our groups.

104

7.0 FUTURE DIRECTIONS

This thesis has highlighted the importance of developing and testing radiotracers for

imaging pathological tau. It also emphasizes the need for further investigation of [18F]AV-

1451 and other tau radiotracers, especially in non-AD tauopathies such as PSP. Testing

should be performed first on brain slices with tau pathology and then successful

radiotracers should move on to clinical studies to validate their use. If none of the

currently developed radiotracers are appropriate for imaging pathological tau in PSP, then

new radiotracers may need to be developed.

7.1 In vitro Testing

[18F]AV-1451 has only been tested on 3 PSP post-mortem brains, based on published

literature. More testing should be done on PSP brain slices using [18F]AV-1451 to

determine if it is capable of binding the straight filaments of tau found in PSP. To verify

this capability experiments such as phosphor screen autoradiography and nuclear emulsion

autoradiography would need to be performed in conjunction with tau

immunohistochemistry staining on adjacent brain slices. [3H]AV-1451 binding assays

should also be performed to assess the binding levels of AV-1451 on PSP brain slices with

tau pathology. Other tau radiotracers that have initially demonstrated high affinity and

specificity for PHF tau (THK5105, THK5117, PBB3, and T808) and these

105

aforementioned in vitro tests would also be appropriate to perform on PSP brain slices.

Ideally, any successful studies would be replicated in order to establish the reliability of

promising results before advancing to clinical studies with PSP patients.

7.2 In Vivo Testing

PET studies are performed on mice prior to human studies primarily to determine a

radiotracer’s brain uptake, washout, metabolism, biodistribution, and excretion. However,

in mutant mouse models, a radiotracer’s binding in the brain may also be assessed. The

rTg4510 mouse model is designed to overexpress the P301L mutant form of tau (linked to

familial FTLD). Tau aggregates in rTg4510 form straight filaments, similar to the

conformation of PSP inclusions. PET scans using [18F]AV-1451 in rTg4510 and wildtype

mice could be performed in order to compare the signal between the two groups and

determine whether [18F]AV-1451 can distinguish the rTg4510 mice. Testing in mice

could also be performed in [18F]THK5105, [18F]THK5117, [11C]PBB3, and [18F]T808 to

determine whether human testing should be pursued.

Following testing in mice, tau radiotracers must be validated in humans. A full kinetic

analysis with arterial input sampling should be performed on a large number of healthy

individuals. This analysis could be used to identify and control for any metabolites that

may interfere with the parent radiotracer signal. Initial studies should have long scanning

times (2 hours or longer, especially for 18F radiotracers) to determine the optimal scan

106

duration for reliable and accurate results. A full kinetic analysis could also be used to

identify an appropriate reference region when testing PSP patients. This would enable the

possible validation of the cerebellum and/or the corpus callosum as brain regions devoid

of specific binding (pathological tau) in PSP. Determination of a suitable reference region

for PSP brains could enable the validation of simplified quantitative methods of analyzing

[18F]AV-1451 binding without arterial input sampling, and possibly the validation of the

semi-quantitative SUVR method. Should SUVR prove to be a reproducible and accurate

method of measuring uptake of [18F]AV-1451 in PSP this would make testing in the

clinical setting much simpler and less invasive.

The final step towards implementing [18F]AV-1451 and other tau radiotracers

([18F]THK5105, [18F]THK5117, [11C]PBB3, and [18F]T808) in clinical settings for

diagnostic, prognostic, and eventually treatment validation purposes, is to perform

multiple large-scale PET studies comparing PSP patients with non tauopathy patients and

healthy controls. These studies would use the previously validated analysis method for the

respective radiotracer. To begin with, it would be important to include PD patients in these

studies because the leading advantage tau radiotracers would provide patients with PSP in

a clinical setting is prevention of a PD misdiagnosis. Tau radiotracers must be capable of

distinguishing PSP groups from PD groups. Other less common disorders than PD, such

as MSA and even the tauopathy CBD, may be included in PET studies, as they have

symptom overlap with PSP. Further testing would then be performed to determine a

radiotracer’s specificity and sensitivity to detect PSP. Radiotracer uptake in PSP should be

compared to their symptoms (both cognitive and motor) to determine if radiotracer

107

binding is capable of predicting cognitive and/or motor decline. Application of a tau

radiotracer as a clinical prognosis tool would prepare patients for the aggressive nature of

PSP and allow them to make necessary changes in order to assist with their daily living

activities. As new tau treatments are currently being developed, there will also need to be

means of testing these treatments in vivo. A validated tau radiotracer can be used in

clinical trials to test the efficacy of the treatment and reduce the size, cost, and time that

large-scale clinical trials require.

7.3 Developing New Radiotracers

Development of new radiotracers is a lengthy and costly process; therefore, it is most

economical to test currently available tau radiotracers on PSP cases. However, should

[18F]AV-1451, [18F]THK5105, [18F]THK5117, [11C]PBB3, and [18F]T808 not be

appropriate radiotracers for imaging pathological tau in PSP, a new tau radiotracer may be

necessary. Initial screening for a fluorescent hit compound should be performed on

straight filaments of tau. The fluorescent compound would then be tested on PSP brain

slices and compared to tau immunohistochemistry staining on adjacent brain slices. If the

compound were to be capable of staining straight filaments of tau, then further

optimization should be performed to increase tau affinity, thereby establishing a lead

compound. Binding assays should be performed to determine the affinity of the compound

for straight filaments of tau over other common CNS targets. The compound must then be

labeled with either 11C or 18F. Ideally the radiotracer will be labeled with 18F due to its

108

longer half-life and portability. Finally, radiotracer pharmacokinetics would be tested in

order to determine if it is capable of crossing the BBB. Lipophilicity (logP 2.0-3.5) and

molecular weight (<500Da) requirements must be met. If the radiotracer adheres to all the

aforementioned criteria then further in vitro testing outlined above (section 7.1) and in

vivo testing (section 7.2) may be pursued.

109

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