Applications of amyloid, tau, and neuroinflammation PET imaging to Alzheimer's disease and mild cognitive impairment

1Alzheimer's disease (AD) is the most common cause of dementia worldwide. It is estimated that by 2050, 1 in 85 people worldwide will develop AD (Brookmeyer, Johnson, Ziegler‐Graham, & Arrighi, 2007). The prodromal phase of AD, defined as mild cognitive impairment (MCI), is characterised by declines in performance of one or more cognitive domains with the preservation of functional independence (Petersen, 2004). Central nervous system (CNS) degeneration and disease neuropathology predates AD and MCI. This is particularly true in presymptomatic carriers of apolipoprotein E (APOE) ε4 (Reiman et al., 2009), which is the leading genetic risk factor for AD (Liu et al., 2014). The hallmark neuropathological substrates for AD and MCI are β‐amyloid (Aβ) plaques and intracellular tau neurofibrillary tangles (NFTs). One major theory, the ‘amyloid cascade hypothesis’, suggests that the overproduction combined with dysfunctional clearance of Aβ is the fundamental event that initiates AD pathogenesis (Hardy & Selkoe, 2002). However, several lines of evidence challenge this assumption. For example, approximately 30% of healthy elderly individuals have significant levels of Aβ deposition without apparent clinical symptoms (Rowe et al., 2010). In AD, histopathological evidence suggests that Aβ levels are a poor predictor of severity of cognitive impairment (Giannakopoulos et al., 2003) and anti‐amyloid interventions have demonstrated limited efficacy in clinical trials (Karran & Hardy, 2014). Presence and extent of hyperphosphorylated tau‐based NFT pathology is positively associated with disease duration and severity of cognitive symptoms (Gómez‐Isla et al., 1997). In addition to the more traditional markers of AD pathology, the existence of neuroinflammation in AD is currently well established. Whilst the initial inflammatory response aims to ameliorate neuronal injury, abnormally prolonged microglial activation can have detrimental effects and potentially serve to exacerbate neurodegeneration (Fakhoury, 2018).

2Positron emission tomography (PET) is a neuroimaging tool designed to measure in vivo molecular processes in the brain. PET radioligands bind a target, such as a receptor, a transporter, or an enzyme. Degree of tracer binding or uptake is used to quantify neuropathology. This technology may be particularly useful for diagnostic purposes, treatment planning, and to assess disease progression in neurological illnesses (Politis & Piccini, 2012). PET biomarkers that have been recommended to improve diagnostic accuracy for AD and MCI include decreased cerebral metabolism on [18F]fludeoxyglucose (FDG) PET and increased Aβ deposition on amyloid PET (Albert et al., 2011; McKhann et al., 2011). Rates of cerebral metabolism do not explicitly elucidate potential disease‐causing neuropathology, but more likely highlight the degree of neuronal activity (Marcus, Mena, & Subramaniam, 2014). Characteristic region‐based patterns of brain hypometabolism have been established within a range of different dementia aetiologies (Kato, Inui, Nakamura, & Ito, 2016; Minoshima, Frey, Koeppe, Foster, & Kuhl, 1995; Moonga et al., 2017; Mosconi et al., 2008; Nestor, Caine, Fryer, Clarke, & Hodges, 2003). PET tracers that measure Aβ burden, tau aggregation, and neuroinflammation provide remarkable insight directly into the processes underlying the pathophysiology of AD and MCI; however, the latter two have not yet been suggested for use in clinical practice. This review describes the recent developments and current applications of PET imaging of amyloid, tau, and neuroinflammation in AD and MCI. Abnormalities on magnetic resonance imaging (MRI) have also been used as clinically relevant imaging markers for AD and MCI (Chandra, Dervenoulas, Politis, & Alzheimer's Disease Neuroimaging Initiative, 2018); however, this is not within the scope of the current work.

3Klunk et al. discovered what would become the most heavily researched amyloid PET radiotracer, 11C‐labelled Pittsburgh compound B ([11C]PiB; Table 1) (Klunk et al., 2004). In their seminal study, it was shown that this benzothiazole‐based radioligand was capable of discriminating between patients with a diagnosis of extremely mild AD and healthy controls. Areas such as frontal and temporoparietal cortex, which are particularly susceptible to Aβ pathology, demonstrated increased [11C]PiB retention in AD. These findings have been replicated, and moreover, recent findings highlight intermediate regional binding of [11C]PiB in MCI when compared to healthy controls and AD patients (Rowe et al., 2010; Villemagne et al., 2013). PiB PET has demonstrated the ability to bind to cored and neuritic Aβ plaques, and also diffuse ones (Ikonomovic et al., 2008).

4Amyloid load as measured by [11C]PiB in vivo has also been associated with other markers of AD including brain atrophy (Archer et al., 2006), medial temporal hypometabolism on [18F]FDG PET, impaired memory performance (Frings, Spehl, Weber, Hüll, & Meyer, 2013), and Aβ pathology from post‐mortem brain tissue (Driscoll et al., 2012). Regarding disease progression, cortical [11C]PiB uptake in frontal, temporal, and cingulate areas was found to be predictive of phenoconversion from MCI to AD (Brück et al., 2013). It is important to concede that MCI patients display a bimodal distribution for amyloid uptake where amyloid load is significantly elevated in a proportion of some MCI patients but not others (Hatashita et al., 2014; Nordberg et al., 2013) (Table 2). Because of this, mean levels of amyloid accumulation may not be the most reliable discriminator in MCI. In line, MCI patients with elevated binding of [11C]PiB were more likely to convert to AD than prodromal patients who were classified as ‘amyloid negative’ (Okello, Koivunen, et al., 2009).

5Noted strengths of [11C]PiB include high amyloid selectivity and affinity, but technical limitations hinder its clinical use in centres without in‐house cyclotrons such as its short decay half‐life (Yeo, Waddell, Khan, & Pal, 2015). Additionally, there are significant operational costs associated with the construction and maintenance of a cyclotron on‐site (Chuck et al., 2005). Data also question the sensitivity of [11C]PiB for the detection of early amyloid pathology when compared to cerebrospinal fluid (CSF) Aβ42 biomarkers. Specifically, it was found on the trajectory of normal ageing that decreased levels of CSF Aβ42 are present before the manifestation of elevated PiB binding (Morris et al., 2010).

6To overcome the inherent impracticalities of [11C]PiB, three novel 18F‐labelled amyloid tracers have been developed: [18F]florbetapir (Amyvid), [18F]florbetaben (Neuraceq), and [18F]flutemetamol (Vizamyl) (Table 1). [18F]flutemetamol has a similar neurochemical composition to [11C]PiB and is actually a derivative of it (Nelissen et al., 2009), whereas [18F]florbetapir and [18F]florbetaben are based on the organic compound stilbene (Hatashita et al., 2014). These tracers display a substantially longer half‐life than that of [11C]PiB, specifically 110 compared to 20 min. This factor in particular removes the main barrier faced by imaging facilities in pursuing in vivo amyloid quantification for clinical use (Jovalekic, Bullich, Catafau, & de Santi, 2016). Moreover, in 2011, the Food and Drug Administration approved use of [18F]florbetapir to aid in the diagnostic process in AD (Yang, Rieves, & Ganley, 2012).

7In line with its suggested clinical utility, the most widely investigated 18F‐labelled amyloid tracer is [18F]florbetapir (Figure 1). It was shown to be comparable to [11C]PiB, with a high degree of correlation between the two in terms of cortical binding (Landau et al., 2014; Wolk et al., 2012). In a group of individuals with a life expectancy of no more than 6 months, [18F]florbetapir showed a sensitivity and specificity of 92 and 100%, respectively, in detecting a significant degree of amyloid plaques. Moreover, the accumulation of amyloid detected by in vivo PET imaging correlated with amyloid pathology measured from brain tissue post‐mortem (Clark et al., 2012). In AD, frontal, temporal, occipital, parietal, cingulate, and precuneus cortical areas all show increased amyloid retention as measured by this ligand compared to healthy controls. This finding also holds for MCI patients in the posterior cingulate cortex (Camus et al., 2012). A strong discriminative ability of [18F]florbetapir for AD and MCI compared to a cognitively normal status is supported by a number of additional studies (Degenhardt et al., 2016; Johnson et al., 2013; Namiki et al., 2015).

8MCI patients rated as Aβ positive using this tracer demonstrated greater longitudinal decline in measures of global cognition, cognitive processing speed, and verbal fluency when compared to Aβ negative patients. AD patients rated as Aβ positive showed a similar pattern on measures of global cognition and verbal fluency (Doraiswamy et al., 2014). Moreover, a positive amyloid scan for MCI patients indicates a greater risk of disease progression to AD (Doraiswamy et al., 2012). While 18F‐labelled amyloid tracers are equivalent in their degree of diagnostic accuracy (Morris et al., 2016), [18F]florbetapir shows particularly fast brain kinetics relative to others (Wong et al., 2010). When considering the clinical implementation of this modality, recent evidence demonstrates its cost‐effectiveness (Hornberger, Bae, Watson, Johnston, & Happich, 2017) and meaningful impact on the diagnostic confidence for AD by physicians (Boccardi et al., 2016).

9While not as widely investigated as [18F]florbetapir, a similar pattern of amyloid retention is demonstrated throughout the cortex in AD using both [18F]florbetaben (Barthel et al., 2011) and [18F]flutemetamol (Nelissen et al., 2009). Although it is not a stilbene derivative, it is important to note that [18F]flutemetamol has demonstrated dosimetry comparable with other 18F‐labelled radiopharmaceuticals (Heurling, Leuzy, Zimmer, Lubberink, & Nordberg, 2016). Additional commonalities shared by these two tracers with [18F]florbetapir include high rates of sensitivity and specificity in diagnosing AD (Barthel et al., 2011; Hatashita et al., 2014; Rowe et al., 2008; Tiepolt et al., 2013; Vandenberghe et al., 2010; Villemagne et al., 2011) and detecting amyloid pathology later confirmed at post‐mortem (Ikonomovic et al., 2016; Sabri et al., 2015; Thal et al., 2015), comparable diagnostic utility to [11C]PiB (Lowe et al., 2017; Villemagne et al., 2012), effectiveness in the prediction of conversion from MCI to AD (Ong et al., 2015; Wolk et al., 2018), and the clinical benefit of increased dementia diagnostic confidence (Schipke et al., 2012; Zwan et al., 2017). Associations are shown between Aβ uptake and cognitive dysfunction in AD (Barthel et al., 2011) and MCI (Bahar‐Fuchs et al., 2013) for [18F]florbetaben. However, there is a still a strong need to further validate [18F]flutemetamol using standardised neuropsychological assessments. Additionally, research suggests that there is a diagnostic advantage of combining structural MRI and PET imaging with this ligand in AD and MCI (Thurfjell et al., 2012).

10There is some disagreement between studies regarding the measurement of Aβ deposition using in vivo PET. This is due to a range of factors including variability in scanning time, methodology employed during analyses, identified reference regions and regions of interest, attenuation correction, partial volume correction, machines used to scan, and tracer‐specific properties. This has led to some uncertainty regarding a consistent definition of abnormal amyloid levels characteristic of AD (Klunk et al., 2015). In support, Villeneuve et al. found that routinely established thresholds for Aβ positivity using [11C]PiB were insensitive and likely to lead to false negatives (Villeneuve et al., 2015). There has been an attempt to solve this problem through the creation of a standardised outcome measure across amyloid‐PET modalities measured in units termed ‘Centiloids’ ranging from 0 to 100 (Klunk et al., 2015). However, there is still a great deal of variability present in the amyloid positivity thresholds derived from this approach (Su et al., 2018). A further limitation of amyloid PET imaging modalities is that there is a lack of concordance with Aβ plaque phases as determined by pathohistological methodology, such as Thal stages (Thal, Rüb, Orantes, & Braak, 2002). For example, [18F]flutemetamol PET was unable to accurate predict the target Aβ stages in 27.84% of cases in an end‐of‐life cohort (Thal et al., 2018). Additionally, an amyloid positivity threshold of a standardised uptake value ratio of 1.5 was not sensitive to MCI patients in Thal Phase 1 or 2, and just lowering this value by 0.2 led to approximately 75% of healthy control subjects being classified as having significant brain amyloidosis (Ismail et al., 2019).

11Advances in the measurement of tau pathology in humans using in vivo PET radioligands are scarce, but recent, when compared to their amyloid‐specific counterparts. For years, the gold standard in quantifying tau‐derived pathology was either histopathological analysis from post‐mortem tissue or the invasive collection of tau and phosphorylated tau from the CSF by means of lumbar puncture (Saint‐Aubert et al., 2017). This delay in applying a tau ligand was due to several major challenges that were inherently present in the imaging of tau. Unlike amyloid, tau is intracellular, so potential ligands need to be able to cross both the blood–brain barrier and the plasma cell membrane of the neurone. In comparison to amyloid pathology, tau aggregates are present in lower concentrations throughout the brain; therefore, ligands are required to have higher specificity for tau. Additionally, tau has multiple protein conformations and isoforms, which could potentially adversely impact sites for ligand binding (Bischof, Endepols, van Eimeren, & Drzezga, 2017).

12The first breakthrough in tau imaging using PET was made using the radioligand [18F]FDDNP. However, this methodology concomitantly assesses amyloid plaque burden and cannot be considered a truly selective for tau pathology (Shoghi‐Jadid et al., 2002), and was later shown to have poor binding affinity for NFTs (Thompson et al., 2009). Since then, four families of tracers in particular have shown promise in the assessment of AD and MCI: [11C]PBB3, from the same tracer family as [11C]PiB, the THK aryquinoline derivatives, the pyrido‐indole derivative [18F]AV‐1451, and recently developed second‐generation tau tracers (Table 3).

13Maruyama et al. validated the use of [11C]PBB3, a pyridinated benzothiazole, in providing high‐contrast imaging of tau inclusions in both animals and AD patients. Using a limited sample of three AD patients and three healthy controls, higher tracer accumulation was reported in medial temporal brain areas of AD patients compared to controls. This included the hippocampus (Maruyama et al., 2013). In vivo tau burden as detected by this ligand is associated with grey matter atrophy and cognitive impairment (Shimada et al., 2017).

14Research suggests that radiotracers from the THK family bind to a different site than that of [11C]PBB3 (Lemoine et al., 2017). The latter may be more selective to tau aggregations that have a spatial relationship with amyloid deposits, while the former may show preference for tau closely linked to brain atrophy, CSF tau, and neuropsychological functioning (Chiotis et al., 2018). The first generation of THK radioligands included [18F]THK523, [18F]THK5105, and [18F]THK5117, but demonstrated several flaws that hindered their clinical utility including poor disease discriminability and high white matter retention. Having overcome these limitations, [18F]THK5317 and [18F]THK5351 may be particularly useful in clinical practice (Saint‐Aubert et al., 2017). [18F]THK5317 shows a good ability to differentiate between AD and MCI patients and healthy controls with a pattern of regional uptake that includes selected areas in the temporal, frontal, occipital, and parietal cortex, in addition to the precuneus (Chiotis et al., 2016). Similar results are shown with [18F]THK5351 with particularly high retention in temporal brain areas (Harada et al., 2016; Lockhart et al., 2016). A direct comparison of these two tracers revealed that [18F]THK5351 has a superior pharmacokinetic profile and greater dissociation from white matter and cerebellar grey matter (Betthauser et al., 2017). However, regional [18F]THK5317 binding has shown an inverse relationship with measures of performance on global cognition and episodic memory (Saint‐Aubert et al., 2016). One should interpret results from THK tracers with caution, given evidence that they bind to monoamine oxidase (MAO). For example, in AD, cortical [18F]THK5351 binding was associated with MAO‐B measured in post‐mortem brain tissue. Moreover, an MAO‐B inhibitor was shown to actively block THK5351 binding in vivo (Harada et al., 2018). Results from another study indicated the detection of suspected off‐target MAO‐B binding for [18F]THK5317 in a study of five AD patients (Murugan et al., 2019).

15Compared to [18F]THK5317, [18F]AV‐1451 (flortaucipir) which is also referred to as [18F]T807, has a clearer pattern of cortical uptake in AD patients and demonstrates a lower degree of off‐target binding (Jang et al., 2018) (Figure 1). However, larger effect sizes for cerebellar uptake were noted relative to [18F]THK5351 (Chen et al., 2018). Binding of this radioligand mirror the distribution of NFT pathology across the Braak stages (Marquié et al., 2017), and was reduced in areas characterised by Aβ neuritic plaques (Lowe et al., 2016). One study found a classification rate of 85.7% for [18F]AV‐1451 in making an accurate clinical diagnosis, including AD and MCI (Passamonti et al., 2017). This tracer also demonstrates high sensitivity and specificity in discriminating AD patients and healthy controls (Maass et al., 2017; Wang et al., 2016). In AD, a similar pattern of cortical uptake is found compared to other tau‐based ligands, whereas patients with MCI demonstrate a more localised pattern of tau deposition in entorhinal regions (Cho et al., 2016; Johnson et al., 2016). While a strength of this radioligand is its specificity for NFT pathology, limitations that pertain to off‐target binding should be acknowledged. A autoradiographic evaluation of [18F]AV‐1451 demonstrated that sites exhibiting off‐target binding included the meninges, scalp, basal ganglia, choroid plexus calcifications, blood vessels and potentially red blood cells, areas characteristic of iron deposition, and leptomeningeal melanin (Lowe et al., 2016).

16Strong evidence implicates tau pathology as measured by [18F]AV‐1451 in the manifestation of dementia‐related cognitive impairment. For example, Pontecorvo et al. show that in a mixed group of Aβ positive subjects, increased tau ligand binding was correlated with global deteriorations in cognition as assessed by the Mini Mental Status Exam (MMSE) and Alzheimer's Disease Assessment Scale‐Cognitive Subscale (ADAS‐Cog) (Pontecorvo et al., 2017). Increased regional tau uptake has also shown associations with impaired domain‐specific neuropsychological performance including memory, language, and visuospatial abilities in numerous variants of AD (Ossenkoppele et al., 2016). Interestingly, patients with a high degree of retention in the entorhinal cortex displayed particularly poor memory functioning (Whitwell et al., 2018). A recent study also links tau burden quantified by [18F]AV‐1451 to neurodegeneration, specifically longitudinal brain atrophy (Das et al., 2018). Other neurodegenerative pathology shows unique profiles of tau aggregation as detected by this tracer. For example, patients diagnosed with corticobasal syndrome show retention increases in frontal and parietal cortices compared to those with MCI due to AD (Niccolini et al., 2018). Considering atypical manifestations of AD, those with posterior cortical atrophy display more cortical uptake in occipital areas relative to logopenic progressive aphasia (lvPPA) patients, whilst lvPPA is characterised by more temporal and frontal uptake (Tetzloff et al., 2018). Despite having a favourable pharmacokinetic profile and specificity for tau, further study is required in determining the clinical utility of in vivo imaging using [18F]AV‐1451 in AD and MCI (Chien et al., 2014; Zhang et al., 2012).

17There is a fast pace of development in the tau PET imaging literature with several novel second‐generation tracers currently in development and undergoing validation (Bischof et al., 2017). Evidence indicates numerous advantages attributed to some of these emergent tau tracers, including having a characteristically low relative affinity for MAO‐B in silico (Murugan et al., 2019). One promising second‐generation tau tracer is [18F]MK‐6240. A high binding affinity was shown to tau deposits specifically in the brain tissue of AD patients, but not to those in the tissue of patients with other tauopathies or TDP‐43, Aβ, and α‐synuclein. However, off‐target binding to neuromelanin was indicated (Aguero et al., 2019). Cortical tau binding for this tracer was two to three times higher in AD subjects compared to healthy controls (Lohith et al., 2019). [18F]PI‐2620 is another recently developed ligand that shows patterns of tau binding in parietal and temporal lobe, in addition to the precuneus and posterior cingulate in AD. A notable lack of off‐target binding, which was an issue faced by tau tracers from prior generations, was also demonstrated (Kroth et al., 2019; Stephens et al., 2018). [18F]JNJ311 and [18F]JNJ‐067 are structurally different from existing tau tracers and published in vivo data on this JNJ family of radioligands is currently unavailable (Leuzy et al., 2019). Wong et al. evaluated three potential tau tracers in amyloid positive AD patients and controls: [11C]RO‐963, [11C]RO‐643, and [18F]RO‐948. Among the three, only [18F]RO‐948 was recommended for further investigation as minimal binding was found in controls and patients with AD had region‐specific patterns of tracer uptake consistent with pathohistological data for the spread of tau pathology (Wong et al., 2018). Not only does [18F]GTP1 retention correspond to established patterns of brain tau distribution in AD, but this new second‐generation tau tracer was also correlated with performance on cognitive measures, particularly in the earliest stages of AD (Bohórquez et al., 2019; Teng et al., 2018; Teng et al., 2019).

18It has been known for some time from the results of histopathological studies that activated microglia localise to Aβ plaques (Perlmutter, Barron, & Chui, 1990) and NFTs (Sheffield, Marquis, & Berman, 2000). Moreover, the results of a genome‐wide association study has implicated the triggering receptor expressed on myeloid cells 2 (TREM2) gene, which encodes a microglial cell surface protein, as the second most significant risk factor for late onset AD after APOE ε4 (Guerreiro et al., 2013). This has stimulated much research into elucidating the role of inflammation in the pathogenesis of AD.

19Advances in the in vivo quantification of neuroinflammation have been made through the investigation of translocator protein 18 kDa (TSPO) as a PET imaging target. Throughout the CNS, TSPO is present in glial and ependymal cells and is located on the outer membrane of mitochondria. It is posited that TSPO is involved in a range of cell‐based functions, including cholesterol transport and hormone synthesis (Papadopoulos et al., 2006). However, its exact role and functional significance in relation to the brain immune response is not fully understood. Under normal physiological conditions, there is a low expression of TSPO limited to glial cells. Nevertheless, during neuronal injury or insult, when microglia are activated, TSPO levels in turn experience a significant upregulation (Rupprecht et al., 2010). Moreover, immunohistochemistry studies have confirmed that TSPO upregulation and microglia activation co‐localise spatially following a neurotoxic intervention, suggesting that TSPO can measure neuroinflammation through the detection of activated microglia (Kuhlmann & Guilarte, 2000). Beyond microglia, increased TSPO expression is also observed in reactive astrocytes (Rupprecht et al., 2010). While numerous TSPO radioligands have been developed so far, we will focus on those investigated in AD/MCI patient populations.

20The most thoroughly researched TSPO radiotracer is [11C]PK11195 (Table 4). In a pioneering study, Cagnin et al. demonstrated that patients with AD had a signature pattern of [11C]PK11195 uptake in brain areas that included the cingulate, temporoparietal, and entorhinal cortex (Cagnin et al., 2001). Since then, results from this tracer have been mixed and somewhat contradictory. Minimal or small clusters of increased binding in MCI and mild to moderate AD was reported in two studies (Schuitemaker et al., 2013; Wiley et al., 2009). These authors speculated that either microglial activation is implicated later in the disease course or [11C]PK11195 is not a sensitive marker of such activity. However, these claims are challenged by findings from studies that used region of interest‐based analyses to show widespread tracer uptake throughout the cortex for AD patients (Edison et al., 2008; Fan, Aman, et al., 2015; Passamonti et al., 2018), particularly in parietotemporal areas (Yokokura et al., 2011) and for MCI patients especially in the frontal cortex (Fan, Aman, et al., 2015; Okello, Edison, et al., 2009). The neurodegenerative effects of this potentially prolonged immune response are hinted at when considering that increased tracer retention has been linked with hippocampal atrophy (Femminella et al., 2016). Utilising [11C]PK11195, longitudinal research suggests an initial reduction of microglial activation in prodromal disease stages (Fan et al., 2017) contrasting with a subsequent increase in microglial activation in AD during disease progression (Fan, Okello, et al., 2015).

21Results pertaining to the relationship between [11C]PK11195 binding and cognitive status are also conflicting. Three studies found inverse correlations between MMSE score and tracer uptake (Edison et al., 2008; Fan, Aman, et al., 2015; Yokokura et al., 2011) and one demonstrates a negative correlation between tracer uptake in the precuneus and episodic memory performance measured by the Rey Auditory Verbal Learning Test (Passamonti et al., 2018), which suggests a role for microglial activation in disease severity. However, others failed to find similar associations between [11C]PK11195 retention and neuropsychological performance (Schuitemaker et al., 2013; Yokokura et al., 2017). This pattern of contradictory results could possibly be attributed to small sample sizes, limitations of the tracer and variability in study methodology.

22Several technical limitations of [11C]PK11195 were found, including its short half‐life of approximately 20 minutes that is a barrier for centres without a costly on‐site cyclotron, low brain uptake, and importantly low signal to noise ratio. It also exhibits a high degree of nonspecific binding (Ching et al., 2012). This nonspecific binding may apply to targets including brain‐based lipids (Hatty et al., 2014) and α1‐acid glycoprotein (Lockhart et al., 2003). Additional clinical translation difficulties are imposed on the molecule through carbon‐11 labelling (Chauveau, Boutin, Van Camp, Dollé, & Tavitian, 2008). However, suboptimal modelling of this tracer is likely its most pressing concern as there has been substantial difficulty in the definition of a true reference region, which is an area absent of binding, for [11C]PK11195 (Chauveau et al., 2008). When using an arterial plasma input function, the use of a reference region allows for valid quantification of binding potential (Cunningham, Parker, Rabiner, Gee, & Gunn, 2005). In fact, variability in the findings of studies discussed using [11C]PK11195 could, at least in part, reflect the use of various and inconsistent methods for reference region quantification. To account for this issue, an automatic supervised clustering procedure utilising a priori kinetic classes has been developed to extract grey matter estimates that can be reliably classified as reference region tissue (Turkheimer et al., 2007).

23The shortcomings of [11C]PK11195 were overcome with the advent of second‐generation TSPO ligands, which notably had an improved signal‐to‐noise ratio and higher binding affinity compared to [11C]PK11195 (Edison & Brooks, 2018). While many second‐generation TSPO tracers have been discovered, we will cover only those that have been most widely used in humans and specifically in AD or MCI. These include [11C]PBR28, [18F]DPA‐714, [18F]FEPPA, [18F]FEMPA, and [11C]DAA1106 (Table 4). Findings from studies examining neuroinflammation, as measured by increased tracer retention, in AD patients using these newer ligands are more consistent than [11C]PK11195. Specifically, increased levels of region‐specific TSPO binding in numerous cortical areas are demonstrated in AD patients compared to healthy controls (Hamelin et al., 2016; Kreisl et al., 2013; Lyoo et al., 2015; Suridjan et al., 2015; Varrone et al., 2015; Yasuno et al., 2008). The temporal pattern of neuroinflammation over the course of the AD has also been well‐characterised through longitudinal investigations. In AD patients, yearly average increases in TSPO binding that ranged from 2.5 to 7.7% were shown using [11C]PBR28 (Kreisl et al., 2016), whilst for [18F]DPA‐714 an elevated annual change of 13.2% in tracer binding was displayed (Hamelin et al., 2018).

24A change in role of activated microglia is supported by a large longitudinal study utilising [18F]DPA‐714 (Hamelin et al., 2016; Hamelin et al., 2018). Participants with MCI and higher initial TSPO binding had a slower rate of decline measured by the Clinical Dementia Rating and smaller increase in TSPO binding than those with lower initial TSPO binding. These results, coupled with the aforementioned [11C]PK11195 studies, have led to the proposal of a dual peak hypothesis of neuroinflammation in AD (Fan et al., 2017). This suggests that the early peak in activated microglia in MCI patients is initially protective, attempting to remove Aβ, whereas the later peak in activated microglia is detrimental. Associations between TSPO expression and clinical outcome for individuals on the spectrum of AD may only be observable for neuroimaging data collected during rising rather than declining phases of these peaks. It is important to note that results from TSPO imaging studies that include MCI patients may reflect either a rising or declining PET signal. While different phenotypes of activated microglia are detectable in pathological studies (Tang & Le, 2016), PET imaging utilising TSPO is unable to differentiate the microglial subtype.

25Similar to [11C]PK11195, in vivo increases in TSPO binding are associated with impairments in global cognition and memory (Hamelin et al., 2018; Kreisl et al., 2013), but also extend to domains that include visuospatial and language ability, and executive functioning, in addition to dementia severity and brain atrophy (Hamelin et al., 2018; Kreisl et al., 2013; Kreisl et al., 2016; Suridjan et al., 2015). However, some studies demonstrate no such correlations with the ADAS‐Cog or MMSE (Yasuno et al., 2008; Yasuno et al., 2012) whilst one reported an opposite pattern, where MMSE scores were positively related to [18F]DPA‐714 binding (Hamelin et al., 2016). This latter result could possibly be attributed to the inclusion of prodromal AD patients in the study sample. Overall, the nature of the correlation between inflammatory changes and cognitive performance requires further investigation. When clarifying regional uptake patterns of second‐generation TSPO ligands in MCI, further research is needed as well. This is due to some studies showing striking patterns of upregulated cortical tracer retention when compared to healthy controls, especially in the temporal lobe (Fan et al., 2018; Yasuno et al., 2012), and others showing no such significant differences (Knezevic et al., 2017; Kreisl et al., 2013).

26It is imperative to acknowledge the primary limitation of these second‐generation tracers, which is their particular sensitivity to a single nucleotide polymorphism (SNP) of the TSPO gene. Genetic variation in the SNP rs6917 results in different patterns of binding affinity to TSPO: high affinity binders, low affinity binders, and mixed affinity binders. Therefore, it is essential to test for these genetic polymorphisms when examining data from these tracers and exclude low affinity binders, which consist of approximately 10% of the population, from analyses (Edison & Brooks, 2018; Yoder et al., 2013). Not being influenced by this TSPO polymorphism can be considered an advantage of [11C]PK11195.

27Overall, tau, amyloid, and TSPO radiotracers show good ability in the detection of AD and MCI, in addition to associated neuropathology (Table 5). However, the role of prolonged microglial activation in the initiation and exacerbation of amyloid and tau neuropathology in AD remains contested. Pro‐inflammatory cytokines, which are produced by microglia in response to neuronal damage (Smith, Das, Ray, & Banik, 2012), experience a chronic activation due to Aβ exposure. This leads to cellular neurotoxicity and potentially to clinical deterioration in AD (Serrano‐Pozo, Betensky, Frosch, & Hyman, 2016). These cytokines have also been implicated in the manifestation of tau in its pathological state, specifically in tau hyperphosphorylation and NFT generation (Maphis et al., 2015). Parbo et al. posit a model where amyloid pathology precedes and activates microglia. Resultant cytokines can then lead to the promotion of tau pathology (Parbo et al., 2018). In support of this, PET imaging with both [11C]PK11195 and second‐generation TSPO ligands have shown positive correlations between regional TSPO binding and amyloid load in AD and MCI (Dani et al., 2018; Fan, Aman, et al., 2015; Fan, Okello, et al., 2015; Hamelin et al., 2016; Knezevic et al., 2017; Parbo et al., 2017; Parbo et al., 2018). Whilst older and using smaller samples, studies indicating negative or no association between microglial activation and Aβ accumulation (Kreisl et al., 2013; Okello, Edison, et al., 2009; Wiley et al., 2009; Yokokura et al., 2011) highlights the need for future research to clarify the nature of this relationship using in vivo brain imaging technology.

28Two very recent studies have sought to investigate whether associations also exist between TSPO binding and both tau retention on [18F]AV‐1451 and amyloid load. The first found no association between the two, but used a sample of patients primarily with prodromal disease (Parbo et al., 2018). The second study evidenced a positive relationship between neuroinflammation and both tau and amyloid pathology in patients with AD and MCI, with similar targeted clusters of cortical regions (Dani et al., 2018). Furthermore, the correlation between tau and neuroinflammation was demonstrated even in participants without significant amyloid burden, suggesting a process independent of Aβ. These results offer the first in vivo evidence in AD and MCI patients that neuroinflammation and tau pathology have a pathophysiological link.

29Molecular imaging with PET has shed light into the complex interplay between Aβ, tau, and neuroinflammation in AD and MCI and helped to clarify to what extent these are part of the normal ageing process or if they represent a distinct pathophysiological process. It has also assessed the patterns of neuropathological regional depositions and their relation to cognitive decline, disease progression and, ultimately, neurodegeneration.

30In vivo imaging of dementia‐related pathology through the reviewed PET radioligands has several benefits. First, a preclinical detection of the disease can be achieved by examining early molecular changes. In addition, the differential diagnosis process between AD and other neurodegenerative disorders can be refined through a focus on characteristic neuropathological mechanisms. From a research perspective, these techniques allow for the study of relationships between cognition and specific neurodegenerative processes across the AD continuum. Finally, these PET molecular targets can serve as surrogate markers for treatment efficacy and may represent potential means of therapeutic intervention.

31This review has demonstrated promising results regarding the role of PET molecular imaging in the diagnosis of AD and MCI and their underlying pathological processes, supporting its use as a research tool and an adjunct in clinical practice. Also highlighted are a number of areas of uncertainty and various tracers' limitations. The interplay between amyloid, tau, and neuroinflammation is an exciting new area of investigation that has only recently become possible through the development of an expanded repertoire of PET tracers. Given the proposal that the role of neuroinflammation in AD pathogenesis changes over the disease course, future multitracer longitudinal studies are required.

32Data collection and sharing for this project, specifically generation of figures depicting PET images, was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol‐Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann‐La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. ADNI was not involved in the writing of this manuscript or the decision to submit the manuscript for publication. Mr A.C., Dr P.E.V., Dr G.P., Dr O.C., and Dr G.D. report no disclosures. M.P. research is supported by Michael J Fox Foundation for Parkinson's Research, Edmond and Lilly Safra Foundation, CHDI Foundation, Glaxo Wellcome R&D, Life Molecular Imaging, Invicro, Curium, Medical Research Council (UK), AVID Radiopharmaceuticals, National Institute for Health Research, Alzheimer's Research UK, and European Commission IMI2 fund.

33The authors do not report any actual or potential conflict of interest relevant to this work.