Two themes will run throughout the chapter. First is an interest in the extent to which PET imaging can inform the distinction between developmental and pathological processes that occur during aging. While the boundaries of aging and dementia pathology remain difficult to separate (Swerdlow, 2007), we believe it worthwhile to view aging as characterized by a multifaceted set of progressive processes that occur at different rates in different people (Buckner, 2004; Hedden & Gabrieli, 2004). Although some of these processes may be the result of developing pathology, such as preclinical Alzheimerās disease (AD) or incipient cerebrovascular disease, others may have similarities to certain diseases while nonetheless arising from a different etiology than the pathological pathway linked to disease, such as dopamine loss in Parkinsonās disease. A recent review by Jagust (2013) discusses the distinction between aging and disease-related processes in detail, and we refer interested readers to this work. We view the distinction as useful in that it sharpens our exploration for distinguishable facets of age-related changes in the brain and their complex, interdependent cognitive sequelae.
The most widely used PET radioligand is 18 F [fluorodeoxyglucose] (FDG), a radioactively labeled glucose analog that enters the pathways for natural glucose consumption in the brain (Phelps et al., 1979) and is a marker of a cellās metabolic rate of glucose. The brain utilizes a disproportionately high amount of glucose, around 25%25 \% of total body glucose, motivating the use of FDG imaging in the study of brain function over the last 40 years. FDG studies in aging and AD have been recently covered in excellent reviews (e.g., Mosconi, 2013; Cohen and Klunk, 2014; Shokouhi et al., 2014). Here, we highlight a few recent approaches that open up future avenues of research in the cognitive neuroscience of aging.
PET measures of glucose consumption are often equated with a general measure of synaptic function, including both synapse density (McGeer et al. 1986, 1990) as well as synapse activity (Schwartz et al., 1979). The primary research focus in aging and AD over the last 20 years has been on hypometabolism within a distinct set of regions comprising temporoparietal cortex and posterior cingulate (Figure 2.1A). These regions display selective hypometabolism in cross-sectional group comparisons between AD patients and cognitively healthy controls (Landau et al., 2011; Bohnen et al., 2012 for review). Longitudinal studies have identified that hypometabolism in AD-vulnerable regions predicts cognitive decline and progression to AD and is consistent with pathological verification of AD diagnosis, which demonstrates the usefulness of FDG PET as a preclinical biomarker of AD (e.g., Hoffmann et al., 2000; De Leon et al., 2001; Herholz et al., 2002; Jagust et al., 2006; Mosconi et al., 2008; 2009; 2010; Ewers et al., 2014).
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Use of multiple PET markers within in the same individuals has provided insight about links between pathological processes and functional disruption. Multi-tracer studies suggest that in cognitively healthy individuals with measurable amyloid burden, regional hypometabolism in AD signature regions is associated with amyloid burden in a dose-dependent fashion (Lowe et al., 2014). However, large-scale studies from different centers find that AD-like hypometabolism in patients with mild cognitive impairment and AD can occur without measurable amyloid burden (Jack et al., 2012). These individuals have been termed āsuspected non-AD pathologyā (SNAP) and can also be identified in about 20%20 \% of clinically healthy older adults (e.g., Knopman et al., 2013). The neuropathologies underlying hypometabolism without amyloid burden remain to be identified. One hypothesis may be that hypometabolism in AD-vulnerable regions reflects neurodegenerative effects of the APOE4 allele that are independent of its well-established relationship to amyloid deposition (e.g., Kim et al., 2009; Morris et al., 2010). Supporting evidence suggests that APOE4 genotype is associated with regional hypometabolism independent of amyloid burden (Jagust et al., 2012; Ossenkoppele et al., 2013; Knopman et al., 2014). Another emerging hypothesis is that tau pathology in aging is associated with hypometabolism. FDG PET changes over time mediate the association between tau markers in cerebrospinal fluid and cognitive decline (Dowling et al., 2015). Human PET imaging of neurofibrillary tau tangles remains in its infancy, but a single case study combining FDG imaging with amyloid and tau imaging found binding to tau, but not to amyloid, spatially overlapped with hypometabolism in areas affected by disease (Ossenkoppele et al., 2015). New developments in tau imaging are reviewed in a later section in this chapter.
It is likely that a portion of this diffuse decrease in FDG signal is due to partial volume effects (e.g., Ibanez et al., 2004; Yananse et al., 2005); but even when methods to control partial volume effects are used, subtle hypometabolism in frontal cortex have been observed (Knopman et al., 2014). A portion of frontal hypometabolism appears to be unrelated to AD pathology (e.g., amyloid plaques; Lowe et al., 2014), and the implications of frontal hypometabolism for cognitive aging are not well established.
Two recent methodological advances open new avenues for research into the relationship between local glucose metabolism as a measure of synaptic integrity and functional connectivity MRI of large-scale brain networks. Simultaneous PET-MRI measurements now allow for time-synced measurements of FDG PET signal and fMRI. Riedl et al. (2014) tested simultaneous FDG PET and resting-state fMRI in two conditions (eyes open and eyes closed) in a between-subject design. Subjects tested with eyes open had higher glucose metabolism in visual cortex, anterior insular cortex, and anterior cingulate, and stronger functional connectivity between these regions compared to subjects tested with eyes closed. Hence, local glucose metabolism may be a correlate of large-scale neural network activity dynamics. A second method that could be used in conjunction with simultaneous PET-MRI measurements allows investigation of changes in glucose metabolism within a scan, on a timescale comparable to that of an fMRI block design (fPET-FDG, Villien et al., 2014). In this functional PET method, the brain is supplied with a constant infusion of FDG rather than the traditional bolus injection, allowing detection of subtle changes in local glucose metabolism in response to task alternations. Combining simultaneous PET-MRI and fPET-FDG in future studies of aging and various cognitive tasks will provide an exciting new opportunity to carefully investigate the metabolic origins of altered fMRI activity and connectivity of large-scale association networks during aging.
Amyloid Burden in Cognitively Healthy Aging
Major strides in understanding the contributions of disease pathology to cognitive aging have been made over the last decade owing to the development of novel classes of PET ligands. Since its initial description of rapid memory loss accompanied by āmiliary fociā (later known as neurotic plaques) and neurofibrillary tangles by Alois Alzheimer in 1906 and 1907 (Alzheimer, 1906; 1907; Maurer et al., 1997), what has come to be known as Alzheimerās disease is characterized by classic amnestic behavioral symptoms. But a definitive diagnosis from the hallmark plaques and tangles could only be confirmed by examination at autopsy until recently. Assays of oligomeric amyloid-beta chains in cerebrospinal fluid can detect lower than usual levels that signal the likely accumulation into neurotic plaques (Andreasen et al., 1999), but the presence and location of the plaques must still be confirmed at autopsy. In 2004, researchers at the University of Pittsburgh and Uppsala University led by Bill Klunk and Chet Mathis reported a PET agent that binds to fibrillar amyloid plaques, revealing the presence and pattern of these hallmark plaques in the living human brain (Klunk et al., 2004). This represented an important step toward using brain markers to definitively diagnose AD in living patients and to evaluating the ability of potential treatments to reduce or eliminate the plaques.
The compound they developed is a derivative of the amyloid-beta binding agent thioflavin-T labeled with carbon-11 that has become known as Pittsburgh Compound B (PIB). PIB has desirable properties, including high affinity, rapid penetration and clearance of nonspecific binding, and construct validity in that binding is elevated in AD (Klunk et al., 2003, 2004; Rabinovici et al., 2007). Several later agents have been developed, with the most widely adopted of this class being fluorine-18 labeled agents, providing a longer half-life to enable shipping to sites lacking a cyclotron. The Federal Drug Administration and European Medicines Agency have approved florbetapir, flutemetamol, and florbetaben for the use of excluding a diagnosis of ADA D. None of these agents are yet indicated for predicting the development of ADA D in individuals with evidence of amyloid plaques or to monitor a patientās response to treatment. New ligands are currently being developed with the aim to increase binding specificity and detect amyloid deposition at very low levels (e.g., Forsberg et al., 2013; Ito et al., 2014).
In the absence of a disease-modifying treatment for AD , the clinical utility of such diagnostic imaging tools remains limited at present. However, as a research tool, the ability to visualize the extent and pattern of amyloid burden in the living brains of individuals opens previously inaccessible research pathways. Clinical trials of antiamyloid therapies are using amyloid PET agents to evaluate the ability of these drugs to clear amyloid plaques, although to date there is little evidence that such clearance improves the cognitive or functional symptoms of patients with AD (Mangialasche et al., 2010; Salloway et al., 2014). Other clinical trials are using amyloid PET agents to limit enrollment to those with evidence of elevated amyloid burden (Sperling et al., 2014a). Such selective enrollment ensures that only individuals with the target pathology are tested, reducing risk for those with little likelihood of being assisted by the investigative treatment and increasing power by eliminating one source of uncontrolled heterogeneity in the population.
The initial and subsequent studies revealed the intriguing observation that approximately 30%30 \% of older individuals with no symptoms of AD nonetheless had substantial amounts of amyloid plaques (e.g., Buckner et al., 2005; Mintun et al., 2006; Johnson et al., 2007; Pike et al., 2007; Figure 2.1B). This observation did not come as a surprise, as the literature has a long history of pathology-burdened cases of individuals who die with no clear symptoms of dementia (Katzman et al., 1988; Morris et al., 1996). Nonetheless, the ability to identify such individuals while they are living has inspired research into what the presence of amyloid burden might mean for otherwise asymptomatic individuals and has led to the development of new guidelines for a preclinical stage of AD (Sperling et al., 2011).
These guidelines imply that the presence of amyloid plaques is sufficient to indicate that an individual is on the pathway to AD or very high risk. This position is concordant with observations in dominantly inherited AD, in which aberrant molecular processes linked to genetic mutations of the genes for presenilin 1 , presenilin 2 , or APP result in the early accumulation of amyloid plaques and to the development of AD (Bateman et al., 2012). Sporadic AD may not be caused by the exact molecular cascades as these genetic variants, but the similarity of the pathological and symptomatic expressions are difficult to dismiss. Individuals with relatively elevated amyloid burden are estimated to have 2.6 -fold higher risk for progression from mild cognitive impairment to AD within two years than are individuals with relatively lower amyloid burden (Jack et al., 2010).
In contrast, categorizing an individual as having preclinical AD on the basis of the presence of amyloid burden alone has been criticized as placing too much emphasis on a single molecular pathway for which insufficient evidence is currently available (e.g., Fjell & Walhovd, 2012). The observation that some individuals pass away after an extended life without any signs of dementia, yet possess the hallmark amyloid plaques, could be taken to demonstrate that amyloid plaques are not sufficient to produce AD . Alternatively, some individuals may have protective factors, or the very formation of plaques may be a mechanism to sequester toxic amyloid oligomers, thereby delaying the impact of soluble amyloid on cognition (Shankar et al., 2008). Whether all individuals with elevated amyloid plaques would eventually develop AD given a long enough life span is ultimately unknowable. For present purposes, the important issue is that amyloid represents a disease-related neuropathology that is associated with age and may be present in a large proportion of the population under study by the cognitive neuroscience of aging.
Associations of Amyloid with Other Brain Variables
The presence of amyloid burden in older adults without signs of clinical dementia has been associated with a number of alterations in brain structure and function. Associations between amyloid and glucose metabolism measured via FDG were reviewed in a prior section. Because of its relevance to memory loss associated with AD , the hippocampus and other structures of the medial temporal lobes (MTL) have been special targets of interest when examining associations with elevated amyloid. Because the hippocampal atrophy rate seen in AD is substantially larger than the rate
in cognitively healthy older adults (Barnes et al., 2009), it may be reasonable to assume that the presence of amyloid in a subset of the cognitively healthy older adults may be responsible for most or all of the association of age with hippocampal atrophy. In cognitively healthy older adults, amyloid has been associated with smaller hippocampal volumes (Apostolova et al., 2010; Mormino et al., 2009, 2014b; Oh et al., 2014) and with longitudinal hippocampal atrophy estimated over follow-up periods between one and four years (Knopman et al., 2013; Mattsson et al., 2014). Individuals with relatively lower hippocampal volume have been estimated as possessing a 2.6 -fold higher risk for progression from mild cognitive impairment to AD within two years compared to individuals with relatively higher volume, similar to that found for elevated amyloid burden (Jack et al., 2010). Specific hippocampal subfields may be preferentially associated with the accumulation of amyloid (Hsu et al., 2015). However, the impact of age on hippocampal atrophy appears to occur independently of amyloid (Nosheny et al., 2015), and significant hippocampal atrophy is observed over one year even in older individuals without evidence of the amyloid burden or cognitive impairment that would suggest preclinical AD (Fjell et al., 2013; Insel et al., 2015). These latter results suggest that presymptomatic AD does not fully explain hippocampal volume loss, but rather that it is part of the normal aging process that can be augmented by a distinct pathological cascade.
In addition to the structural changes observed, aberrant functional activity in the MTL has been associated with elevated amyloid. In older individuals with elevated amyloid burden, MTL activity tends to be reduced during associative memory tasks (Vannini et al., 2012, 2013; Huijbers et al., 2014). Memory-associated cortical
regions, many overlapping with what has come to be known as the default network, also exhibit amyloid-related alterations (Buckner et al., 2005; Sperling et al., 2009, 2010; Mormino et al., 2012), although the direction of the results is not always consistent (Kennedy et al., 2012; Adamczuk et al., 2016, Elman et al., 2014a).
Because task-based activation may vary widely depending upon the task administered during scanning or the analysis methods used, measures of network function based on intrinsic properties of network connections have gained prominence. Elevated amyloid burden has been associated with disruptions of such functional connectivity, particularly of connections between the MTL and cortical regions within the default network (Hedden et al., 2009; Sheline et al., 2010; Mormino et al., 2011; Jack et al., 2013; Huijbers et al., 2014). These associations in cognitively healthy individuals are mirrored by alterations that occur in individuals carrying dominantly inherited mutations prior to the onset of symptoms (Chhatwal et al., 2013). Although most attention has been focused on within-network connectivity, emerging evidence suggests that alterations in between-network connections are also associated with amyloid burden (Elman et al., 2014b), as are disruptions of a regionās participation across multiple networks (Buckner et al., 2009; Drzezga et al., 2011). Although the network connectivity dynamics are highly simplified relative to humans, work in transgenic rodent models has found that expression of amyloid plaques is associated with reduced regional connectivity (Bero et al., 2012), or that impairments in functional connectivity precede development of plaques (Grandjean et al., 2014). However, it is important to note that an association between functional connectivity and elevated amyloid is not always observed (Adriaanse et al., 2014), and there is some evidence that connectivity may be increased rather than decreased in individuals with elevated amyloid (Lim et al., 2014; Mormino et al., 2011). These inconsistencies and the small effect sizes observed in many studies indicate that we still lack a full understanding of the relationship between amyloid burden and functional connectivity and how they might inform the separation of clinically normal aging from AD. Using advanced imaging techniques that enable simultaneous acquisition of multiple slices for faster sampling, dynamic measures of non-stationarity in brain states may further inform our understanding of the relationship between amyloid and functional connectivity (Jones et al., 2012). There may also be interactions between the presence of amyloid and neurodegeneration that impact functional connectivity (Jack et al., 2013).
Although there is evidence of increased white matter abnormalities in AD patients (Brickman et al., 2009; Brickman, 2013; Brun & Englund, 1986; Provenzano et al., 2013) and an association between vascular amyloid deposition and WMH (Gurol et al., 2013), studies in cognitively healthy older adults indicate that amyloid and white matter degeneration are independent brain biomarkers. Multiple reports have failed to find an association between amyloid burden and white matter hyperintensities (Rutten-Jacobs et al., 2011; Hedden et al., 2012, 2016; Lo et al., 2012; Marchant et al., 2012) or diffusion-weighted imaging of white matter integrity (Kantarci et al., 2014) in cognitively healthy older adults. Although one study found an association of amyloid with impairment in axial diffusivity in some white-matter structures, no associations were observed in other diffusion measures (Molinuevo et al., 2014). In contrast, another study found increased integrity on measures of fractional anisotropy and mean diffusivity in multiple structures associated with amyloid burden
(Racine et al., 2014). Additional studies have found amyloid burden and white matter abnormalities to be independently associated with cognitive function in control or patient groups (Hedden et al., 2012; Barnes et al., 2013; Haight et al., 2013; Guzman et al., 2013), which is again suggestive of separate pathological pathways for amyloid and white-matter abnormalities. Evidence from transgenic rodent models is mixed with regard to an association between amyloid and white-matter-integrity (Kastyak-Ibrahim et al., 2013; Qin et al., 2013; Sun et al., 2014; Grandjean et al., 2014), and it is difficult to lean heavily on rodent models given they replicate features of pathology but do not recapitulate the full physiology of the human conditions.
To our estimation, the weight of the current evidence favors amyloid burden and white-matter abnormalities evolving along separate pathological trajectories that may nonetheless co-occur in some individuals and combine to impair cognition. However, because individuals with both amyloid and white-matter abnormalities may have more prominent cognitive symptoms and would therefore be less likely to be included in a sample of cognitively healthy individuals, researchers must be aware of the potential for sampling bias. Such a bias would account for finding no association in the preclinical stage of AD with an association emerging once symptom onset is observed. A focus in future research on longitudinal associations between amyloid and whitematter abnormalities will help answer important questions as to whether these two neuropathologies have a causal relationship.
Longitudinal studies that examines change in amyloid with change in other neuroimaging markers will become an important frontier for future research. Because of the nonlinear function that characterizes amyloid accumulation (with acceleration of accumulation early on, eventually reaching a plateau), there will be a dynamic window in which such associations may be observed, with reduced associations once individuals reach the accumulation plateau. Cross-sectional studies commonly categorize individuals into those with and without evidence of elevated amyloid burden because of the nonnormal distribution of this variable. These methods may also require a reexamination when longitudinal studies require subtle indicators of amyloid accumulation over time.
Amyloid-Cognition Relationships
Using amyloid imaging PET techniques, individuals who possess a marker of AD pathology can be identified and examined specifically or culled from studies of cognitive aging. Such individuals have previously been included in samples used to study aging as a developmental process, representing approximately 30%30 \% of the cognitively healthy individuals in the population. Markers of amyloid burden therefore provide an opportunity to examine hypotheses with more precision than was previously possible. Additionally, hypotheses involving differential impacts of AD-related pathology and age-related developmental processes can be examined.
For example, one question that can be tested is whether asymptomatic older individuals with evidence of amyloid plaques exhibit greater decrements in cognition than do those without evidence of amyloid plaques. This has been a question of intense interest, with many studies examining multiple cognitive domains (e.g., Hedden et al.,
2012; Lim et al., 2012; Rodrigue et al., 2012). Although both positive and negative results have been published, the general consensus from studies with large sample sizes (i.e., those with N > 100\mathrm{N}>100 ) is that amyloid burden is associated with subtle cognitive deficits and that certain domains are more strongly impacted by amyloid than other domains.
In a meta-analysis that included 24 independent cohorts (up to 3,495 subjects) where amyloid burden was assessed using a variety of methods (including PET imaging, cerebrospinal fluid assays, and pathological examination), episodic memory was found to be most impacted by the presence of amyloid in asymptomatic individuals (Hedden et al., 2013). Global cognitive function (which includes episodic memory) and executive function were also significantly impacted by the presence of amyloid, while processing speed, working memory, visuospatial cognition, and semantic memory were not significantly impacted. When examining only studies employing the PET agent PIB, only episodic memory was significantly impacted by the presence of amyloid. It is important to keep in mind that, although significant, the effect size of amyloid on episodic memory observed in the meta-analysis was approximately r=0.1\mathrm{r}=0.1 (equivalent to Cohenās d=0.2d=0.2, Cohen 1988). This small effect size implies an association that explains approximately 1%1 \% of the variation in episodic memory. While the associations are expected to be relatively subtle, as by definition asymptomatic individuals do not exhibit grossly abnormal cognitive performance, weak associations between markers of hallmark AD pathology and cognition pose problems for a clear separation of clinically normal aging and AD.
Additional studies examining associations of amyloid with longitudinal cognitive change have been published since 2013 (Mormino et al., 2014; Ossenkoppele et al., 2014; Dowling et al., 2015). Based on these collective results, it appears likely that the presence of amyloid in asymptomatic older individuals has at least some specificity for subtle memory deficits, and that assessment of longitudinal change in cognition may be somewhat more sensitive to the distinction of normal aging and AD-related processes than cross-sectional assessment. This trend may again reflect biased sampling in cross-sectional studies because individuals with amyloid and cognitive symptoms would be less likely to enter a sample of cognitively healthy individuals.
Markers of amyloid burden also allow cognitive aging to be explored in the absence of evidence of comorbid neurodegenerative disease. A natural question is whether the subtle deficits in memory and other cognitive domains associated with the presence of amyloid burden account for most or all of the cognitive alterations typically ascribed to the aging process. That is, has the cognitive aging literature mischaracterized normal age-related processes by including individuals with preclinical amyloid pathology? There are two primary ways to examine this question. One is to remove all cognitively normal individuals with evidence of elevated amyloid burden from the sample and examine whether age effects continue to be observed as significant. Studies taking this approach have observed significantly lower cognitive functioning with increasing age even in those individuals without evidence of preclinical AD (e.g., Nebes et al., 2013). Again, this finding complicates the notion that cognitive decline in aging is primarily driven by pre-symptomatic disease, at least insofar as the available markers adequately identify early stages of disease.
A different approach is to estimate the impact of both age and amyloid on cognition in a combined model and examine the effect of age while controlling for amyloid. If age continues to have a significant effect, it supports a model in which age has a significant independent association with cognition irrespective of amyloid burden. The same logic has been applied to the investigation of age-related decreases in neurotransmitter functions and their impact on cognition, as reviewed later in this chapter. This approach flexibly accommodates the inclusion of multiple markers of brain alterations that may be associated with cognition so that the model can be used to understand whether age continues to account for significant variation in cognition after controlling for many such markers. This approach also maximizes power by using the full dataset of aging individuals without making any assumptions about the future progression of an individual along a particular pathogenic pathway.
If we take this second perspective, the meta-analytic data on the association between amyloid and cognition can be combined with that from meta-analytic data on the association of age and cognition to estimate the likely impact of the inclusion of individuals with amyloid on estimates of the age effects on cognition. Taking data from a large meta-analysis that looked across multiple domains (Verhaghen and Salthouse, 1997), we find that the cross-sectional association of aging with cognition is 2-62-6 times as large as that of amyloid with cognition (Figure 2.2). Even if measured amyloid burden shared all of its cognition-related variance with that of the age-related variance in cognition, amyloid would only account for less than 50%50 \% of the age-related variance in cognition. Because the correlation between amyloid and age is less than r=0.4r=0.4 in most samples of cognitively healthy older adults (e.g., Pike et al. 2011; Kantarci et al. 2012; Hedden et al. 2016), it is more likely that amyloid
Figure 2.2 Relative estimated effect sizes of amyloid burden on cognition and age on cognition across several cognitive domains. Assuming a correlation of r=0.4r=0.4 between amyloid and age, the percentage (numbers at right) of age-related variance in each cognitive domain that is shared with amyloid can be computed. Meta-analytic estimates for amyloid are based on Hedden et al., 2013; meta-analytic estimates for age are based on Verhaeghen & Salthouse, 1997.
shares less than 20%20 \% of the age-related variance in cognition. Although this is not insubstantial, it indicates that there is much more to be learned in the cognitive neuroscience of aging than discriminating between older individuals with and without preclinical AD, with the caveat that measures of amyloid burden are imperfect estimates of preclinical AD.
In a recent cross-sectional study that combined multiple brain markers, including FDG and amyloid PET, to examine age-related differences in cognition, amyloid was found to share 16%16 \% of the age-related variance in memory (Hedden et al., 2016; Figure 2.3). This accords well with the above meta-analytic estimate. In the same study, FDG shared 27%27 \% of the age-related variance in memory (Figure 2.3), which could be taken to support a model in which FDG has a more proximal relationship to memory function than does amyloid (Jack et al., 2013). However, the relationship of amyloid or FDG to memory in cognitively healthy older adults should be placed in context with their relationship to each other and to other markers of brain health. For instance, of the 16%16 \% of age-related variance in memory shared with amyloid, 6%6 \% was
Figure 2.3 Percentage of age-related variance in episodic memory performance that is shared with different brain markers. Highlighted sections indicate molecular markers of glucose metabolism (FDG) and amyloid (PIB). Entorhinal and parahippocampus indicate thickness measures. Hippocampus and striatum indicate volume measures. Functional connectivity measures are estimated from the default network (DN) and frontoparietal control network (FPCN). White matter integrity measures were estimated using fractional anisotropy from diffusion tensor imaging (DTI) and white matter hyperintensity (WMH) volume. Pie sections indicate proportional share of age-related variance in episodic memory estimated for each brain marker from univariate analyses. Adapted from Hedden et al., 2016.
also shared with FDG and 11%11 \% was shared with hippocampus volume; only 3%3 \% was found to be uniquely attributable to amyloid (Hedden et al., 2016). Amyloid markers may indicate likelihood of early progression on the AD spectrum, but because they are associated with age, they may also have interactive relationships with other brain measures that alter their relationship to cognition across age.
Amyloid is only one of the two hallmark pathologies associated with AD. Hyperphosphorylation and misfolding of the tau protein lead to aggregation visualized as tangles inside of neurons. This aggregation may be more closely linked to neurodegeneration than are amyloid plaques (Ossenkoppele et al., 2015; Spires-Jones and Hyman, 2014) and may occur through a distinct pathway than does amyloid accumulation (Small and Duff, 2008). Relating back to the discussion in a previous section as to whether amyloid plaques are sufficient to indicate whether an individual is on the path to AD , recent studies have turned to tau tangles as an equally, or possibly greater, predictor for the transition to AD . A recent development is the introduction of novel tau imaging agents (e.g., Shao et al., 2012; Chien et al., 2013; Okamura et al., 2013; Chien et al., 2014, Fawaz et al., 2014; Figure 2.1C). Early observations suggest that measurements using these agents recapitulate the spread of tangles from the entorhinal cortex to nearby limbic regions and finally to neocortical regions in a fashion consistent with Braak staging (Braak and Braak, 1997; Villemagne et al., 2015). However, several of these agents display binding in unexpected locations, and it is as yet unclear how specific each agent is to various subspecies of tau. Nonetheless, because the localized expression of neurofibrillary tangles occurs throughout life from a relatively early age (Braak and Braak, 1997; Nelson et al., 2012), tau may be a particularly important molecular target for understanding age-related alterations in cognition (Delacourte et al., 2002). The rapid adoption and integration of tau imaging into large-scale studies and clinical trials indicates the level of excitement
generated in the field by the development of these novel agents for an important target (Sperling et al., 2014b; Villegmagne et al., 2015).
Imaging Dopamine
Many PET agents target neurotransmitter systems, providing a window into another class of synaptic changes in aging. Age-comparative studies have identified reduced densities of serotonin receptors (e.g., Wong et al., 1984, Meltzer et al., 1998, Yamamoto et al., 2002) and acetylcholine receptors (e.g., Dewey et al., 1990). Cholinergic cell death has been implicated in the cognitive deficits associated with AD (e.g., Terry and Buccafusco, 2003, for review). More than any other neurotransmitter system, however, the dopamine system has generated substantial interest because of its relevance to human cognitive functions, psychiatric disease, aging, and age-related movement disorders. In 2000, Arvid Carlsson, Paul Greengard and Eric Kandel shared the Nobel prize for the discovery of dopamine as a neurotransmitter and the mechanisms by which neurotransmitters like dopamine contribute to synaptic plasticity, the neurochemical basis of learning and memory. Dopamine is synthesized in the midbrain and heavily innervates the striatum and, to a lesser degree, cortical areas. Striatal cells receive input from widespread areas across the cortex and project back to cortical areas via the pallidum and thalamus forming āloopsā that broadly differentiate into motor, cognitive, and affective systems. Nigral dopamine neurons modulate neural excitability in cortico-striatal circuits by promoting behaviors for maximizing reward and minimizing punishment (Schultz et al., 1997; Frank and OāReilly, 2006).
In cortical regions, dopamine neurons form structural complexes with other neurons similar to those described for striatum. However, the functional implications are less understood than in striatum, and it remains to be explored exactly how cortical and striatal dopamine signals jointly or independently regulate behavior. One hypothesis regarding prefrontal dopamine functions in PFC is that they stabilize neural representations in working memory and render them robust against interfering distractors (Servan-Schreiber et al., 1990; Durstewitz et al., 2000). This hypothesis was suggested by early primate studies (Brozoski et al., 1979; Sawaguchi and GoldmanRakic, 1991) that demonstrated that dopamine depletion in prefrontal cortex (PFC) selectively impaired working memory in monkeys.
Dopamine Functions Decrease Across the Adult Lifespan
Post mortem examinations of human brain tissue suggest a linear reduction of around 5%5 \% per decade in dopamine concentration and cell density for cognitively healthy subjects between ages 50 and 90 (Carlsson and Winblad, 1976; Riederer and St Wuketisch, 1976; Fearnley and Lees, 1991). Linear age-related reductions in dopamine cell counts are distinct from the pathophysiology in Parkinsonās disease, which is characterized by exponential and regionally specific neuronal death (Fearnley and Lees, 1991).
The introduction of PET imaging approaches to infer dopamine signaling in the mid 1980s catalyzed research (Farde et al., 1986). With respect to aging, PET studies estimating age-related changes across the lifespan align well with postmortem cell counts, and these data have been extensively reviewed (Reeves et al., 2002; BƤckman et al., 2006). Striatal markers of the main postsynaptic receptor types D1 and D2, as well as the presynaptic dopamine transporter (DAT), show reliable differences between young adults in their 20s and older adults over the age of 65 (Figure 2.4).
The few studies that have assessed binding of more than one radioligand in the same set of subjects show that presynaptic DAT and postsynaptic D 2 receptor binding are correlated at around 0.6-0.7 (Volkow et al., 1998a; Ishibashi et al., 2009). Further, lower density of markers of the dopamine system correlates with lower density of other neurotransmitters (Wang et al., 1995), synaptic function measured by FDG PET (Volkow et al., 2000) and even white matter integrity (Rieckmann et al., 2016). Lower neurotransmitter densities in the dopamine system are also paralleled by age-related reductions of muscarinic receptors (e.g., Dewey et al., 1990) and glutamatergic receptors (Segovia et al., 2001). The available literature therefore suggests evidence for a global breakdown of synaptic efficiency across the adult lifespan that may be reflected in decreased terminal density and receptor availability, with the dopamine system being just one facet reflective of this global cascade.
The broad reductions in markers of the dopamine system in aging are unlike the patterns seen in disease. For example, in Parkinsonās disease, presynaptic DAT markers show pronounced decreases compared to age-matched controls, whereas the postsynaptic D2 receptor functions remain relatively intact or show a subtle increase (Seeman and Niznik, 1990; Kim et al., 2002). Intact or up-regulated postsynaptic receptor functions in response to DAT depletion have also been demonstrated for DAT knock-out mice (Gainetdinov et al., 1999) and explain why dopamine boosters like L-dopa are effective treatments in Parkinsonās disease. Conversely, in schizophrenia, presynaptic DAT density is unaltered whereas the postsynaptic D2 receptor response to dopamine release is increased compared to controls (Seeman and Niznik, 1990; Laruelle 2000).
There are, however, several observations that complicate the idea of a global neurodegeneration in aging with respect to the dopamine system. First, PET studies of ligand binding to dopamine-synthesizing enzymes suggest that dopamine synthesis capacity is unaltered or even increased in clinically healthy aging (e.g., Braskie et al., 2008). Higher dopamine synthesis in aging may reflect compensatory responses to the deficits in other parts of the dopamine system, e.g., the pre- and postsynaptic transporter and receptor functions. Second, in an age-comparative study that utilized a D1 receptor tracer with affinity for both striatal and cortical D1 receptors, individual differences in striatal D1 receptors did not correlate with individual differences in cortical D1 receptors in older adults (Rieckmann et al., 2011a). These findings suggest that while striatal and cortical dopamine receptor functions show similar trajectories with chronological age, they appear to be regulated independently. Third, there is mixed evidence for a progressive linear loss of dopamine markers in old age. While there are reliable differences in pre- and postsynaptic dopamine markers between younger and older adults, in some studies focused on older adults only (Reeves et al., 2005; van Dyck et al., 2008) or using samples with very tight age ranges (Nevalainen
Figure 2.4 Example PET images for three common PET ligands of the dopamine system. An example image for a young person (20-30 years) and a clinically normal older adult ( > 65>65 years) is shown for each ligand. Loss of striatal signal for the old person can be seen for all ligands. Images are voxelwise DVR images with reference region cerebellum. (See color plate also)
et al., 2015) no association between dopamine markers and age was observed. These findings may suggest that, across the lifespan, loss of dopamine functions may be pronounced in middle adulthood and then level off in old adulthood. This pattern contrasts with that observed for white-matter integrity, hippocampal atrophy (e.g., Walhovd et al., 2005), and cognitive functions (Schaie, 1996; Salthouse, 2014), where negative age associations accelerate in old age. Identifying the true rate of decline of dopamine functions in aging, as well as comparisons between within-person trajectories for dopamine markers of striatum, cortex, midbrain dopamine synthesis and atrophy can ultimately only be addressed with longitudinal data. At least two largescale longitudinal dopamine PET studies are currently underway, which will likely yield new insights into age-related dopamine losses over the next decade (Parkinson Progression Marker Initiative, 2011; Nevalainen et al., 2015).
PET has also taken on an important role for translation from recordings in animal systems to understanding complex human behavior. PET-based measures of striatal dopamine functions are associated with learning from feedback, adapting behavior in response to rewards (Schott et al., 2008; Cools et al., 2009; Jonasson et al., 2014), and flexible updating of memory representations in prefrontal cortex (inferred by fMRI, Nyberg et al., 2009). Prefrontal dopamine D1 receptor densities measured with PET have been linked to working memory task performance in schizophrenic patients (Abi-Dargham et al., 2002) and in controls (Takahashi et al., 2008). PET studies of the human dopamine system have shown associations of markers of the dopamine system with complex cognitive operations such as playing a video game (Koepp et al., 1998), general knowledge (Karlsson et al., 2011), and human personality traits like impulsivity (Buckholtz et al., 2010).
Markers of the Dopamine System are Associated with Cognitive Functions in Aging
The first empirical demonstration that reduced cognitive functions in older adults are in part explained by individual differences in markers of striatal dopamine functions was reported in Volkow et al. (1998b). In this study, 30 cognitively healthy volunteers between the ages of 24 and 86 participated in one PET scan assessing striatal D2 receptor densities and a neuropsychological exam including tests of executive functions, perceptual speed, and finger tapping, which all showed reliable performance decreases with increasing age. Partial correlation analyses showed that even after accounting for the effects of chronological age, dopamine D2 receptor availability and cognitive performance on tests of executive and motor function (see also Wang et al., 1998) were significantly correlated. BƤckman et al. (2000) extended these initial findings to show that episodic memory is also associated with lower striatal D2 availability. Moreover, the amount of variance in tests of perceptual speed and episodic memory related to the effects of chronological age (between 13%13 \% and 52%52 \% ) was almost fully explained by the dopamine PET measure. These results are interesting in light of recent multimodal imaging explorations that link multiple MRI and PET-based measures of brain structure and function to age-related variance in cognitive functions, explaining around 75%75 \% of the age-related variance (Hedden et al., 2016, discussed in the section on amyloid).
Dopamine PET markers were not available in that analysis, and it remains to be explored in future studies how dopamine PET markers interrelate with other markers of a cognitively healthy aging brain, or how much variance in cognitive performance can be attributed selectively to individual differences in dopamine functions.
The initial findings reported in Volkow et al. and BƤckman et al. of an interrelation between age, dopamine, and cognition have been extended to different cognitive tasks (Reeves et al., 2005) and ligands for DAT (Mozley et al., 2001; Erixon-Lindroth et al., 2005; van Dyck et al., 2008) and dopamine synthesis capacity (Braskie et al., 2008; Landau et al., 2009).
Dopamine PET can also be used to assess the change in ligand binding displacement following acute release of endogenous dopamine into the synapic cleft (Laruelle, 2000; Monchi et al., 2006). Few studies have used radioligand displacement to investigate dopamine release in the context of aging. An exception is a study by Karlsson et al. (2009), who showed that while an executive task successfully decreased radioligand binding to striatal dopamine receptors in younger adults, no significant change could be observed in older adults. Although it is unclear whether decreases in D1 binding are indicative of dopamine release or receptor internalization, this study suggests that older adults not only have decreasing numbers of dopamine transporters and receptors but also an altered response of the dopamine system to a cognitive challenge. Consistent with an interpretation of reduced dopamine release in older adults, Floel et al. (2008) demonstrated a change in ligand binding during task performance in older adults only when dopamine synthesis was boosted by a dose of levodopa prior to the scan. However, it remains to be seen how these results can be reconciled with the work by Braskie et al. (2008) suggesting a compensatory increase in dopamine synthesis capacity in cognitively healthy older adults. Future studies may provide an answer by focusing on individual differences that link dopamine synthesis capacity, cognitive performance, and response to pharmacological challenge in older adults.
Multimodal imaging. Longitudinal PET studies of the dopamine system in aging are not yet available, and it is important to keep in mind that the hypothesis that dopamine loss and declining cognitive functions are linked in aging has thus far exclusively been addressed in cross-sectional comparisons. It is not obligated that the relations among individual differences in change of neurobiological and cognitive measures follow the same patterns suggested by between-person cross-sectional differences (e.g., Nyberg et al., 2010; Lindenberger et al., 2011). Until large longitudinal data collections are available, cross-sectional multimodal imaging studies combing PET, fMRI and, where possible, pharmacological interventions can provide potential mechanisms by which dopamine losses in aging may affect cognitive performance.
In one study in cognitively healthy older adults, Landau et al. (2009) showed that striatal dopamine synthesis capacity was related to working memory task accuracy as well as the strength of prefrontal cortex activation during the task as measured by fMRI. Age-related changes in prefrontal activation during working memory are well established (e.g., Rajah and DāEsposito, 2005) and the results of Landau et al. provide initial evidence for an interrelation of prefrontal activation and cognitive performance with the integrity of the dopamine system. These findings were later extended to demonstrate an association between dopamine synthesis capacity and prefrontal coupling to striatum during working memory (Klostermann et al., 2012). Using a D1 receptor
ligand, BƤckman et al. (2011) and Rieckmann et al. (2011b) showed that the relation of dopamine functions with fMRI task activation and functional connectivity, respectively, are found in widespread association networks. These data could suggest that striatal dopamine functions are a critical modulator of efficient frontal-parietal recruitment during cognitive task performance. The causal nature of this association was assessed by administering a D1 receptor antagonist to the sample of young healthy participants to examine whether the patterns of activation and connectivity typical for older adults could be elicited in younger adults by blocking postsynaptic dopamine receptor functions. The older adultsā patterns of activation (Fischer et al., 2010) and functional connectivity (Rieckmann et al., 2012) could, at least in part, be simulated by drug administration.
The combination of multimodal imaging and pharmacological manipulation is an underutilized experimental design that is of great advantage beyond its usefulness to provide support for causal relations. Even after accounting for macrostructural partial volume effects (Meltzer et al., 1990), it is a legitimate concern that age-related differences in brain morphology, regional signal-to-noise, or biases in co-registration of imaging data to group templates introduces factors that may induce a spurious association between PET and MRI signal. Pharmacological challenges in within-person comparisons of younger adults do not suffer from this problem and can therefore provide an important complementary examination of neurotransmitter imaging results in age-comparative studies.
Prefrontal Dopamine Functions. The striatum receives the densest dopamine innervations from the midbrain, and molecular PET binding targets like DAT and the D2 receptor are preferentially available in striatum. D1 receptors are also densely concentrated in striatum but are the dominant receptor type in cortex, which can be seen in Figure 2.4. Low availability of binding targets in cortex is not a primary concern for molecular studies of prefrontal dopamine functions. Rather, almost all dopamine receptor ligands are non-selective to a certain degree and also bind to serotonin receptors. This is of little effect in the striatum, where there are few serotonin binding sites, but likely affects the cortical signal of dopamine receptor ligands to some extent and is an important caveat to consider.
Age differences in dopamine receptor densities are comparable for striatum and cortical areas (Suhara et al., 1991; Rieckmann et al., 2011a) but may have distinct implications for cognitive decline. In line with animal research that suggests a role for prefrontal dopamine functions in maintenance of stable representations, it has been proposed that prefrontal dopamine functions become particularly apparent in task situations that assess consistency of responses from one trial to the next (MacDonald et al., 2009, 2012). Inter-individual response variability across many trials increases with advancing age, and MacDonald et al. showed that extrastriatal D1 and D2 dopamine receptor densities mediate this effect. Critically, in these studies striatal dopamine receptor densities were unrelated to inter-individual response variability, which suggests that striatal and extrastriatal dopamine receptors are, in part, independent measures in aging. Indeed, Rieckmann et al. (2011), showed that while striatal and extrastriatal D1 receptor densities are correlated in younger adults, this is not the case for older adults. This effect was not apparent in older adults who were comparable to younger adults in terms of performance
on an executive functions task. In other words, receptor losses in cortex and striatum do not always go hand in hand, and the level to which striatal and prefrontal dopamine functions are āin balanceā may also be an important component in understanding the role of dopamine functions in complex cognitive tasks. Further exploration of this hypothesis opens up a new avenue in the context of aging research but is at the core of the dopamine hypothesis for schizophrenia, which postulates a hyperactivity of striatal dopamine functions, and a hypoactivity of prefrontal dopamine functions and of understanding the symptoms of Parkinsonās disease (pronounced loss of striatal dopamine neurons relative to prefrontal dopamine neurons).
Conclusion ēµč«
This selective overview highlights the importance of quantifying the spatial distribution and concentration of molecular targets and their relation to other brain markers for unraveling the association between aging and cognition. Much of aging research to date is based on correlational analyses with a focus on associations, rather than on causative mechanisms. To the extent that neurobiological aging can be characterized by gradual progression or development of cellular and molecular processes, molecular imaging techniques will provide the most proximal tools to these processes. Their potentially causative roles must still be verified with longitudinal studies paired with statistical methods allowing causal inference or, ideally, direct pharmacological intervention studies that target selective molecular pathways.
Molecular targets of pathological proteins, such as amyloid and tau, may help to reveal how aging processes and preclinical AD pathology have overlapping, interactive, or distinct impacts on cognition. By relating synaptic or neurotransmitter function as measured with FDG, dopaminergic targets, or other neurotransmitter systems to macroscopic or functional changes using multimodality imaging, we may be better positioned to inform how large-scale brain networks are differentially impacted by molecular systems during aging, and how these neural alterations underlie cognitive outcomes. Recent advances that allow simultaneous PET-MRI acquisition, more sensitive PET cameras, and multiple tracer techniques may enable new approaches to technical challenges. The field should continue to design a future in which we apply these developing tools and our emerging understanding to predict those individuals most likely to be affected by a particular type of pathology or disruption of a specific molecular pathway. Such studies may provide a foundation for the best application of interventions as they emerge.
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