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Q J Nucl Med Mol Imaging. Author manuscript; available in PMC 2012 Mar 1.
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PMCID: PMC3290913

FDG- and amyloid-PET in Alzheimer’s disease: is the whole greater than the sum of the parts?


The development of prevention therapies for Alzheimer’s disease (AD) would greatly benefit from biomarkers that are sensitive to subtle brain changes occurring prior to the onset of clinical symptoms, when the potential for preservation of function is at the greatest. In vivo brain imaging is a promising tool for the early detection of AD through visualization of abnormalities in brain structure, function and histopathology. Currently, positron emission tomography (PET) imaging with amyloid-beta (Aβ) tracers and 2-[18F] fluoro-2-Deoxy-D-glucose (FDG) is largely utilized in the diagnosis of AD. This paper reviews brain Aβ- and FDG-PET studies in AD patients as well as in non-demented individuals at risk for AD. We then discuss the potential of combining symptoms-sensitive FDG-PET measures with pathology-specific Aβ-PET to improve the early detection of AD.

Keywords: Alzherimer disease, Positron-emission tomography, Cell aging

Alzheimer’s disease (AD), the leading cause of dementia in the elderly, is a neurodegenerative disorder with insidious onset and progressive declines in memory, attention and language.1 At present, AD affects approximately 10% of individuals 65 years of age, with the prevalence doubling every 5 years up to age 80, above which the prevalence exceeds 40%.2 In 2007, more than 26 million people had AD worldwide, a number that is expected to double every 20 years up to 81 million in 2040 because of the anticipated increase in life expectancy, as the baby-boomers generation ages3,4. It has been estimated that interventions that delay the clinical onset of dementia by 1 year would reduce the prevalence in 2050 by 9 million cases.5 To date no therapy has been shown to halt or reverse the underlying disease process once AD has been diagnosed, and treatment is confined to symptomatic palliative interventions instigated after the disease process is well underway.

Longitudinal studies of normal individuals who go on to develop AD show that there is a somewhat abrupt transition in cognitive symptom decline between the preclinical stage and the early stage of AD. Gradual cognitive decline in the preclinical stage reaches an inflection point that gives way to a comparatively steep loss of cognitive abilities, which is the hallmark of clinical AD.6 Typically, the relatively rapid loss of cognitive abilities following the inflection point is what leads family members or caregivers to bring patients in for evaluation. However, this kind of “tipping point” suggests that by the time patients come in for diagnosis, too much irreversible brain damage may have already occurred for any treatments to be effective. Interventions, once they are developed, ideally would be implemented long before symptoms occur. While risk factors such as apolipoprotein E and family history have been identified, their predictive value remains to be established, and its presence may not be enough to justify the potential risks of medical interventions (as they become available) in non-symptomatic individuals. Therefore, another major goal in AD research is the identification of diagnostic markers, especially for the preclinical stages of disease when symptoms are not yet apparent. As discussed by Hampel et al. (2010),5 “In clinical trials, biomarkers that are indicators of the central or downstream elements of AD pathogenesis could serve at least three different purposes: as diagnostic biomarkers, to detect and monitor effects of drug candidates on the disease process, and as safety markers to detect and monitor potential side effects of drug candidates at an early stage”.

Currently, the provisional diagnosis of AD remains largely one of exclusion, although increasingly it is becoming one of inclusion as gains in the understanding of the pathology underlying AD are made. Clinical history, neurological and psychiatric examination, cognitive testing and structural neuroimaging all serve to exclude other common causes of dementia. Inclusionary factors include diagnostic criteria such as those found in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (Text Revised), or the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria.1 Longitudinal follow-up can also help confirm a typical course of AD. Laboratory studies, such as thyroid-function tests, rapid plasma reagin and serum vitamin B12, are necessary to rule out treatable causes of dementia, while computed tomography (CT) and magnetic resonance imaging (MRI) are used to exclude alternative causes of dementia, such as brain tumor, subdural hematoma, and cerebrovascular disease such as cerebral infarcts and white-matter lesions. In early cases, neuropsychological testing can help to obtain objective signs of memory disturbances, although there has been disagreement on what pattern of cognitive disturbance constitutes a “typical” AD profile, as opposed to memory loss due to other factors.

A definite diagnosis of AD can only be made by neuropathology, which is regarded as the gold standard, and is based on postmortem detection of specific pathological lesions: amyloid-beta (Aβ) plaques in the extracellular space and blood vessels, intracellular neurofibrillary tangles (NFT), neuronal and synaptic loss in specific brain regions.7 Ironically, we ultimately define the disease with pathological criteria, but we have hardly any information in this regard during life since there are no tests for the definitive diagnosis of AD in vivo. As a result, patients may be misdiagnosed with AD when in fact they have another dementia, or may be left undiagnosed.8 The lack of standardized diagnostic tests for AD greatly limits the potential for an accurate diagnosis, and even more so, for early detection.

Changes in brain histopathology, and consequently in brain structure and function, are known to precede the signs and symptoms of disease by many years. Neurodegeneration in AD is estimated to begin 20–30 years before any clinical manifestations of disease become evident.912. According to a popular theoretical model in AD, the “amyloid cascade hypothesis”,13 Aβ plaques and NFT load increase during this preclinical phase, either by overproduction or reduced clearance, causing synapse loss and neuronal death. In light of recent findings that Aβ fibrils do not appear to be the main promoter of neuronal degeneration,13 the amyloid hypothesis was reformulated by stating that Aβ oligomers confer neurotoxicity to neurons by disrupting nerve signaling pathways in AD.14, 15 While the causes of AD are being investigated, consensus exists that the medial temporal lobes, which are critically involved in the neural control of memory functions, are most vulnerable to AD pathology.10, 12, 1618 The posterior cingulate, parieto-temporal and frontal cortices become affected later in the course of disease, in keeping with progression of clinical symptoms.12 The local and distant effects of AD pathology on tissue physiology impair neuronal function in these vulnerable regions,19 eventually causing global cognitive impairment and dementia.20 Despite degeneration of central nervous tissue, the physical health of the individual seems to be unaffected by AD pathology; often AD patients will live out full life spans, suffering many years of dementia that take great financial, emotional, and physical tolls not only on patients but their families as well. Thus the ability to detect and medically intervene at the earliest signs of AD is of vital need.

Imaging techniques can be invaluable in diagnosing disease, as they are generally non-invasive and relatively easy for patients to tolerate. Brain imaging is a promising tool for the early detection of AD, particularly thanks to its ability to characterize the temporal succession of anatomical involvement along with disease progression. Our ability to use neuroimaging to visualize brain changes arising along with AD progression has improved dramatically over the years thanks to technological and instrumental improvements. Twenty years ago, only indirect evidence of the neurodegenerative AD process could be imaged with MR, which is used to measure structural tissue loss (i.e., atrophy). Additionally, for many years, PET was the only method to measure presumably indirect functional effects of neurodegeneration in AD, using 2-[18F]fluoro-2-Deoxy-D-glucose (FDG) as the tracer to measure cerebral metabolic rates of glucose (CMRglc) as a proxy for neuronal activity. During the past 10 years, PET tracers for fibrillar Aβ, the principal constituent of AD senile plaques, have become largely available and AD pathology can now be directly visualized in vivo. Among PET tracers for Aβ plaques, the best known tracers are N-methyl-[11C]2-(4’-methylaminophenyl)-6-hydroxybenzothiazole, aka Pittsburgh Compound-B (PIB),21 4-N-[11C-methyl] amino-4’-hydroxystilbene (SB13),22 2-(1-96-(2-18F-fluoroethyl)(methyl)amino)-2-naphthyl)ethyldene) malono nitrile (FDDNP)23 and more recently the trans-4-(N-methyl-amino)-4´-{2-[2-(2-[18F]fluoroethoxy)-ethoxy]-ethoxy}-stilbene (BAY94-9172).24 Among these tracers, PIB binds to fibrillar Aβ plaques with high affinity,25 and has become the most widely utilized and best characterized in terms of tracer kinetics, modeling, and analytic methods.

In what follows, we are going to summarize and compare results from FDG- and Aβ-PET (mostly PiB) studies in AD. We first describe the use of these techniques in their most common clinical application, as a tool to help in the differential diagnosis of active AD. Then we describe attempts to use these techniques in the early diagnosis of preclinical AD individuals, given the presence of various known risk factors for AD.

FDG and Aβ-PET in the distinction of ad from normal aging and other dementias

Some decades ago the largely accepted paradigm was that people with AD pathology had dementia and people without AD pathology did not. This simple division started blurring when pathology studies revealed plaques and tangles in a sizable fraction of elders who had died with their cognition intact, and that the number, density and location of amyloid plaques were not particularly correlated with either AD symptoms or severity.2628. Biomarker and longitudinal aging studies of the past 20 years swept aside this old binary view in favor of a more complex and dynamic picture. At present, the majority view holds that both pathologic and clinical changes occur gradually over time, and that, while there can be no clinical symptoms without AD pathology, there may be AD pathology without clinical symptoms. Thus, an imaging-based diagnosis of AD, as distinct from normal aging, requires some functional measure of pathological change that can be correlated with cognitive symptoms. As such, it is most important to use imaging-detected biomarkers that capture both the extent of AD pathology and its effects on brain function, and therefore cognition.

Functional imaging measures may also help in the differential diagnosis of AD from other neurodegenerative dementias. The ability to differentiate AD from other dementia syndromes in the living patient is invaluable for offering an accurate prognosis, treatment options and risk to family members of contracting the disease in future. While clinical history, laboratory tests, and MRI are successful in ruling out dementias that are secondary to other medical disorders (such as HIV, vitamin B-12 deficiency, or depression, to name a few), such clinical measures are notoriously inadequate in distinguishing AD from other primary neurodegenerative causes of dementia, such as frontotemporal dementia (FTD) or Lewy Body dementia (DLB), or even from dementia resulting from cerebrovascular disease (CVD), all of which can have significant neuropathological overlap with AD.29, 30 To date, attempts at using clinical criteria such as history, psychometric values and structural imaging measures to distinguish among the dementias yield sensitivity and specificity rates ranging from 49–90%, depending on the population studied.31, 32 Functional imaging measures increasingly help improve sensitivity and specificity in differential diagnosis, both by identifying pathognomonic patterns of functional brain changes among the dementias as well as by directly visualizing biomarkers specific to disease state

FDG-PET and AD diagnosis

Metabolic imaging studies prove quite successful in correctly diagnosing AD. On FDG-PET, AD is characterized by a specific regional pattern of CMRglc reductions in the parieto-temporal areas,33, 34 posterior cingulate cortex (PCC),35 and MTL.36 As the disease progresses, frontal association cortices become involved, while cerebellum, striatum, basal ganglia, primary visual and sensorimotor cortices remain preserved.34, 36 The extent and regional distribution of hypometabolism may vary across subjects, and hemispherical asymmetries are often noted,36, 37 especially at the early stages of AD. This in vivo pattern of hypometabolism is found in the vast majority of clinically diagnosed AD patients, and in over 85% pathologically confirmed AD cases.34 An interesting finding is that CMRglc is highly correlated with clinical disabilities in dementia.38 Clinical AD symptoms essentially never occur without CMRglc decreases, the extent of which is related to the severity of cognitive impairment].39, 40 Moreover, despite some overlap, the characteristic AD-pattern of CMRglc reductions yields high sensitivity in distinguishing AD from controls,41, 42 from other neurodegenerative dementias, including FTD and DLB,34, 42 and from cerebrovascular disease.43 In a large multi-center study of NL, AD, FTD and DLB, individual FDG-PET scans were processed using automated voxel-based methods to generate disease-specific patterns of regional FDG uptake.42 These standardized disease-specific PET patterns correctly classified 95% AD, 92% DLB, 94% FTD, and 94% NL.42 The method yielded high discrimination accuracy in patients with mild dementia as well as moderate-to-severe dementia.42 Altogether, these findings support the use of FDG-PET in the differential diagnosis of the major neurodegenerative dementing disorders.

Direct fibrillary Aβ imaging and AD diagnosis

Several PIB-PET studies demonstrated significant PIB retention in AD patients compared to controls, mostly in the frontal cortex, parieto-temporal, PCC/precuneus, occipital lobes, thalamus and striatum,25, 4447 consistent with the known pattern of Aβ plaques deposition observed at post-mortem. Significant PIB retention is found in over 90% clinically diagnosed AD patients, in as many as 60% of MCI and up to 50% of NL elderly.4450 Interestingly, AD and normal subjects can be easily dichotomized as showing either significant (PIB+) or absent PIB retention (PIB−), but hardly show intermediate levels.45, 46 This could facilitate interpretation of PIB-PET scans for clinical use. The presence of a PIB+ pattern has been shown to improve the differential diagnosis of AD from FTD and from Parkinson’s disease.47, 51 However, significant PIB retention is observed in DLB44 and in patients with cerebral amyloid angiopathy (CAA).52 The impact of vascular amyloid on PIB signal is particularly relevant in view of using PIB, or other Aβ tracers, in the early detection of AD. A study showed that PIB is not specific for dense, classical plaques,53 but rather binds to a family of amyloid substrates ranging from diffuse plaques to plaques in the vascular system (i.e. CAA).54

The correlation between PIB retention and cognition is generally fairly weak,55 consistent with the notion that Aβ plaques distribution does not correlate with clinical symptoms in AD.56 Additionally, the few published longitudinal PIB-PET papers indicate a lack of progression of PIB uptake in NL, MCI and AD.5759 AD patients apparently reach a plateau in PIB retention, despite progression of their clinical symptoms and worsening of hypometabolism on FDG-PET.57 Jack et al. (2009)58 examined longitudinal PIB-PET in NL, MCI and AD and showed that the rate of PIB change did not differ by clinical group. The lack of longitudinal progression suggests that PIB deposition could be an early event during aging and disease.60 However, lack of change also suggests that PIB and similar tracers may not be the best option for longitudinal studies (discussed below). In this respect, the UCLA FDDNP tracer appears to have an advantage over PiB, since FDDNP studies showed some longitudinal effects,23 strong correlations with scores on memory and global cognition, and finer grading than PIB measures in MCI patients.61 These findings may be due to the fact that, unlike PiB, FDDNP binds both Aβ fibrils and NFT,61 shows a cortical binding pattern similar to PIB, and additionally binds to the MTL.23 However, FDDNP has low specific to non-specific binding ratio23 which makes the scans more challenging to interpret for clinical use.

Early detection of AD using FDG- and amyloid-PET

Both FDG- and amyloid-PET can distinguish the presence of AD pathology in affected individuals, each showing its own image-derived biomarker that has fairly high sensitivity and specificity. The question then becomes whether these methods can be used to detect similar changes in non-demented individuals who would go on to develop AD. Large-scale longitudinal studies of community-based populations would be the ideal for determining how early AD pathology can be seen in people who eventually develop AD. However such studies would be extremely costly for a relatively low yield. Instead, both longitudinal studies of clinic-based populations (who are more likely to have significant risk factors than the general population), and studies of normal individuals with previously determined risk factors have been performed. Results from these studies suggest that biomarkers suggestive of AD pathology may, in fact, be detected in non-demented individuals, although the predictive power of these biomarkers has yet to be determined.

Decline from normal cognition to AD

A few longitudinal FDG-PET studies of cognitively normal (NL) elderly monitored the progression of some to MCI and AD compared with those who remained normal. These showed that CMRglc reductions in hippocampal and parietal regions precede the onset of dementia by many years.6265 Hypometabolism in these regions predict decline from NL cognition to MCI/AD with over 80% accuracy.62, 63 Progressive CMRglc reductions were observed years in advance of clinical symptoms in a clinico-pathological series of subjects followed with longitudinal in vivo FDG-PET scans from normal cognition to the clinical diagnosis and to post-mortem confirmation of AD.65 More work is needed to establish how early FDG-PET deficits become detectable in the course of disease. Nonetheless, published studies show that non-demented individuals with reduced CMRglc are at increased risk for developing AD.6265 which supports the use of FDG-PET in the early detection of AD.

The utility of amyloid-PET for early detection of AD pathology in non-demented individuals is much less understood. Currently, one published PiB-PET paper showed that higher cortical PiB retention at baseline predicted clinical progression from CDR 0 to CDR 0.5 at the 2-year follow-up.66 There are no reports of longitudinal effects on PiB-PET during disease progression. Using the FDDNP tracer, longitudinal progression effects were reported for 3 non-demented subjects that deteriorated over 2 years, including one subject that declined from normal cognition to MCI, and 2 MCI patients that converted to AD.61 More studies are needed to assess the predictive value of amyloid tracers in the conversion to AD and their relationship to metabolic changes.

Early detection in individuals with known risk factors

The vast majority of AD cases are sporadic in nature, with no familial clusters and the greatest risk factor being the age of the patient.67 However, several other factors have been identified that are known to increase an individual’s risk for AD significantly. Functional imaging studies of non-demented individuals who possess one or more of these risk factors demonstrate that similar patterns of both CMRglc reductions on FDG-PET and increased tracer retention on PiB-PET or with other Aβ tracers occur prior to the onset of AD symptoms, in both genetic early-onset and late-onset AD forms. PET imaging studies in these at-risk populations are summarized in Table I and described below.73100

Table I
FDG- and PIB-PET findings in preclinical AD.

Early-onset familial AD and genetic mutations

Presymptomatic individuals carrying autosomal dominant genetic mutations in the amyloid precursor protein (APP, located on chromosome 21), presenilin 1 (PSEN1, on choromosome 14) and presenilin 2 (PSEN2, on chromosome 1) genes are at greater risk for early-onset AD. These genetic mutations are very rare, accounting for approximately 1% of AD cases in the population, have nearly full penetrance and an early age of onset, usually before 65 years.68 Over 98% of AD cases in the general population are unrelated to any of these genes, suggesting that other genetic and non-genetic mechanisms act in concert to trigger symptoms onset in the majority of patients.56, 68 On FDG-PET, presymptomatic persons carrying autosomal dominant genetic mutations associated with early-onset familial AD show the typical AD pattern of hypometabolism compared to age-matched mutation non-carriers.6972 FDG-PET abnormalities were observed up to 13 years prior to the onset of symptoms in APP and PSEN2 mutation carriers.72 On PIB-PET, higher Aβ load was observed in asymptomatic and symptomatic individuals carrying PSEN1 and APP mutations as compared to controls.73 PIB retention was especially high in the striatum of mutation carriers as compared to controls and to sporadic AD patients.73 Symptomatic mutation carriers showed significant tracer retention also in the cortical regions, although uptake was not as high as in sporadic AD subjects.73 These findings suggest that amyloid deposition in PSEN1 mutation carriers may begin in the striatum before the onset of symptoms, and later spread to neocortical regions.73, 74 On the other hand, the data may also point to different patterns of Aβ deposition in AD, with the striatum being more affected in early-onset AD and the cortex being more affected in the late-onset forms. A similar pattern of prominent PIB retention in the striatum, especially in the caudate nucleus and putamen, was also found in patients with APP mutations.75

Mild cognitive impairment

Patients with Mild Cognitive Impairment (MCI) are considered as being high risk for late-onset AD (age at onset after 65 years), especially for those individuals with severe memory deficits (i.e., amnestic MCI).76 Among MCI patients, those presenting with more pronounced, or more AD-like, CMRglc reducti1ons decline to AD at higher rates than those who do not show hypometabolism.35, 7779 CMRglc reductions in MCI predict future AD with 75–100% accuracy.35 On an individual basis, a PET diagnosis positive for presence of AD was shown to correctly predict future cognitive deterioration with accuracy greater than 84%,80, 108 while a negative PET scan correctly indicated that clinical deterioration was unlikely to occur in 75–79% of the cases.108, 109 These data indicate a tight relationship between a specific pattern of metabolic abnormalities and conversion to dementia, which can be evaluated on a subject by subject basis and may therefore be of value in clinical practice.7779, 8183 PiB-PET studies in MCI consistently show an uptake pattern comparable to patients with AD in approximately half of the MCI patients, whereas the other half shows a pattern more similar to healthy controls.4450 Some studies have shown that PiB uptake is higher in amnestic compared to non-amnestic MCI, possibly reflecting increased risk for AD.84 Although so far the prognostic value of these findings is not completely clear, a few studies showed that those MCI patients who later converted to AD had higher PIB retention compared to stable MCI at baseline.8587 These results point to a potential predictive value of PiB for early diagnosis of AD.

Apolipoprotein E genotype

Asymptomatic individuals carrying the epsilon 4 allele (ε4) of apolipoprotein E gene (APOE) located on chromosome 19 are at higher risk for late-onset AD. Of the three common human APOE isoforms, ε2, ε3, and ε4, the APOE- ε4 genotype is over-represented in late-onset AD and is associated with an earlier age of onset compared to the other genotypes.88 Despite its well-established association with AD, the ApoE ε4 genotype has no clear familial pattern of transmission and appears to act as a risk modifier by lowering the age at onset of clinical symptoms, rather than as a causal determinant.68 CMRglc deficits resembling those in clinical AD patients have been observed in NL individuals at genetic risk for AD. Non-demented individuals carrying an Apolipoprotein E (ApoE) ε4 allele have CMRglc reductions as compared to ApoE ε4 non-carriers.9093 CMRglc deficits in NL ApoE ε4 carriers are progressive, correlate with reductions in cognitive performance,89, 92 and occur in young adulthood.93 In addition, there is evidence that the ε4 allele leads to greater longitudinal CMRglc declines during conversion to MCI or AD.62, 78 A recent PiB-PET paper in middle aged to old NL individuals showed increased PiB retention in ApoE ε4 carriers compared to non-carriers and in relation to the ε4 allele dose.94

Family history of late-onset AD

Asymptomatic individuals with a first degree family history of late-onset AD, in absence of known genetic mutations, are also at increased risk for AD. While the complete etiological picture of AD remains unresolved, the inheritance of predisposing genetic factors appears to play a major role also for the late-onset form of disease. After advanced age, family history is the second greatest risk factor for AD, with some variability depending on which family members are affected.9597 Children of affected parents are at especially high risk of AD, as confirmed in multiethnic studies.96 FDG-PET studies showed that, among NL individuals with family history of late-onset AD, those with a maternal history of AD (i.e., only the mother had AD) present with progressive CMRglc deficits in AD-vulnerable regions compared to those with a paternal history (i.e., only the father had AD) and those with negative family history of any dementia,98, 99 possibly reflecting increased risk for developing the disease. Interestingly, NL children of AD fathers did not show CMRglc abnormalities at both cross-sectional and over time.98, 99 These findings were independent of age, gender, education and ApoE genotype. Figure 1 shows how having a family history of AD affects brain metabolism in different clinical groups. Genetic mechanisms involved with maternally inherited CMRglc reductions are under investigation 21. Since the first FDG-PET paper in 2007, several studies have now replicated the “maternal effect” in late-onset AD and extended the observation to other modalities. A PiB-PET paper in middle aged to old NL individuals showed increased and more widespread PiB retention in those with a maternal history compared to those with a paternal history and negative history of AD (Figure 2).100 MRI studies have shown increased atrophy and altered white matter integrity in NL with AD-mothers compared to those with AD-fathers and controls.100103 These results add biological evidence to epidemiological findings showing that, while both maternal and paternal transmission are present in AD families, maternal transmission is more frequent and is associated with higher risk of AD, poorer cognitive performance and a more predictable age at onset in the offspring.21

Figure 1
FDG-PET scans in 6 representative individuals with negative (FH−) or positive (FH+) first degree family history of late-onset AD, including two cognitively normal (NL) individuals, two patients with MCI, and two patients with AD. A reduction in ...
Figure 2
Statistical parametric maps (SPMs) showing higher PiB retention in asymptomatic individuals with a first degree family history of late-onset AD compared to controls with negative family history of any dementia. Areas of increased PiB retention are represented ...


In order to use any tracer for clinical, and most importantly, preclinical detection of AD and for clinical trials, several attributes of the proposed radioligand must be taken into account. As summarized in Table II, the main characteristics are:

Table II
Comparing operating characteristics of FDG and PiB PET tracers

Specificity to AD pathology

FDG-PET has very low specificity to AD, as metabolic reductions can be found in other dementias, neurological or psychiatric disorders. In presence of metabolic abnormalities in absence of post-mortem or pathology data, doubt would remain as to whether CMRglc reductions are due to AD or other causes. Amyloid-PET imaging has instead high specificity for AD pathology.

Sensitivity to AD

FDG-PET has high sensitivity to distinguish AD from controls and from other neurodegenerative diseases, as well as in discriminating individuals at higher vs lower AD risk, and yields good quantitative and topographical correlation with clinical progression. In contrast, PiB-PET has low sensitivity to distinguish AD from normal aging, as typical amyloid lesions and positive PiB scans are found in both demented and non-demented individuals, including up to 50% of NL elderly.11, 12, 104106 Based on presence of significant PiB uptake alone, it would not be possible to accurately diagnose AD from normal cognition, thus recommending use of amyloid imaging to the differential diagnosis of AD from other disorders, especially FTD. In this scenario, amyloid imaging seems suitable to rule out AD in the presence of a PIB negative scan, since a demented patient without brain Aβ cannot have AD-dementia by definition. However, amyloid imaging may not be sufficient to rule in AD. If a patient with uncertain diagnosis is PIB+, it would not possible to distinguish between AD, DLB and CAA based on PIB alone. With respect to early detection of AD, non-demented cases with substantial AD pathology are often described as a ‘preclinical’ AD group given the absence of cognitive abnormalities.11, 106 However, many NL elderly with brain Aβ deposits never develop dementia in life.107 Amyloid imaging is necessary for the early detection of Aβ pathology, but is not sufficient to make an early diagnosis of AD-dementia. This observation brings up the important, and often overlooked, discrepancy between AD-pathology and AD-dementia.108 Those NL and MCI showing an AD-like PIB pattern (and therefore Aβ pathology) are conceivably at higher risk for developing AD-dementia as compared to individuals without brain Aβ pathology. However, having Aβ plaques does not equal to being at a ‘pre-dementia stage’, and the prognostic value of increased Aβ load on PET has to be established. Since Aβ imaging is a relatively new technique, there aren’t enough published longitudinal PIB-PET studies to draw conclusions on its preclinical value in AD (Table I).

Correlation with clinical symptoms

While FDG-PET measures strongly correlate with clinical symptoms, the relationship between PiB retention and cognitive performance has been relatively weak, consistent with evidence that neuronal degeneration is a stronger predictor of dementia than AD pathology107. Therefore, functional tracers like FDG-PET, whose signal correlates well with cognitive impairment, may be needed to appreciate the extent to which Aβ is affecting brain function. Non-demented individuals showing both increased Aβ load and reduced CMRglc would be the ideal target population for prevention studies in AD (Figure 2).

Progression effects

FDG-PET measures show longitudinal effects in clinical AD patients,109 MCI,79 in NL elderly declining to MCI and AD compared to non-decliners,62, 63 and in non-demented individuals at risk for AD.92, 99 In contrast, amyloid tracers generally are either present or absent, and do not show progression effects.5760 It was hoped that Aβ imaging would facilitate the study of the time course of amyloid deposition in brain. However, people appear to either have substantial brain Aβ or not, and to remain relatively unchanged over time. This could be due to the fact that Aβ deposition is a very early event in AD. Should this be the case, then amyloid tracers may be more useful for longitudinal examination of younger individuals with minimal tracer uptake, who may still show progression effects. Lack of longitudinal effects may also be due to technical issues, such as the intrinsically low spatial resolution of PET scanners, or to the fact that PIB uptake reflects the presence of Aβ fibrils, but not fibrils’ dimension or growth.110, 111 With respect to technology, head-dedicated scanners typically have a spatial resolution of 3–5 mm full width at half maximum (FWHM; i.e. reconstructed or “apparent” resolution), for a ”real” resolution of a few additional millimeters. The direct consequence of low spatial resolution is a partial loss of signal (partial volume effect) in structures that are smaller than twice the FWHM of the tomograph. In other words, source activity in structures with diameter ≥2× FWHM (≥10 mm) can be correctly resolved, while an underestimation of the activity concentration occurs for smaller structures. Aβ plaques are in the order of µm. Capturing changes within this order of magnitude may not be feasible with the resolution of a typical PET scanner. Additionally, PIB uptake reflects the presence of Aβ fibrils, but not fibrils’ dimension or growth.110 Ever since the first validation studies,112, 113 PIB analysis has been based on simplified reference tissue models from receptor studies, which treat Aβ, a polymer, as if it were a receptor.111 There is a substantial conceptual difference between imaging the density of Aβ fibril polymers and neuronal receptors. The concept of Aβ molecular imaging probes was introduced as a new paradigm that goes beyond classic binding potential parameters to include binding characteristics to polymeric peptide aggregates.110, 114 This would ideally increase resolving power in characterizing the progression of Aβ, especially for subjects presenting with substantial uptake at the first examination.

Imaging tau pathology would be particularly important, as NFT progression follows the expected pattern of regional involvement based on clinical symptoms,12 and unlike Aβ plaques,NFT load correlates with cognitive impairment in AD.115 Except for the FDDNP tracer, which binds to both plaques and NFT,23 in vivo imaging of NFT is still under development.

Early detection effects

A large body of literature has shown an early detection capability for FDG-PET studies, going as far as detection of CMRglc abnormalities in at-risk individuals in their 40’s.93 It is not known how early in the course of disease Aβ depositions can be detected. Except for the known presence of amyloid deposits in young individuals with Down’s syndrome.116, 117 Aβ plaques are more prevalent in brains of individuals older than age 50 years.118, 119 At present, two published PET studies reported increased PiB retention in middle age to old asymptomatic individuals at increased risk for AD.94, 100 The predictive value of these abnormalities remains to be established.

Positive predictive value

Studies have reported a high predictive value of FDG-PET for cognitive decline.80, 82, 120 There are currently no studies that examined PiB-PET accuracy to predict future decline on an individual basis.


In order to be accepted in clinical practice, a tracer must be widely accessible. The key difference between carbon-11 and fluorine-18 radionuclides is their rate of decay or their decay ‘half-life’. This physical parameter determines both how quickly the radiolabeled form disappears from the body and how far they can be distributed from the point of radiochemical production. The decay half-live of carbon-11 is approximately 20 min and that of fluorine-18 is approximately 110 min. Only a minority of PET centers world-wide have the on-site capability of producing high specific activity carbon-11-labeled products, and need to rely on external production of fluorine-18 radiotracers (such as FDG) by regional cyclotron facilities that distribute the radiotracers to local scanners. The approximately 110 min half-life of fluorine-18 allows distribution within a 2–4 h travel radius, whereas the 20 min half-life of carbon-11 does not. As such, PiB and other 11C-ligands are not widely available, and several companies are now developing 18F-amyloid ligands. Thus far, these tracers have shown a similar uptake pattern to PiB, but lower specific to non-specific binding ratio,24 which may further complicate detection of longitudinal and early AD effects.


Once available, early interventions in preclinical AD will rely highly on the ability to detect the earliest signs of pathology in an individual who is still cognitively normal, and to distinguish AD from other neurodegenerative processes. Diagnostic tools with adequately high sensitivity and specificity will allow clinicians to target preventative measures specific to the disease process manifested in each patient. In patients already showing symptoms, the accurate characterization of the extent and nature of brain damage, based on converging evidence from different biomarkers, will likely play an important role in the prediction of subjects‘ clinical course. Other potential benefits include the selection of individualized treatment plans and screening of patients with more uniform underlying pathology for targeted research and drug trials.

Thanks to the technique’s sensitivity to progression effects, FDG-PET is a prime candidate modality for detecting functional brain changes in preclinical and early AD. Nonetheless, given the low specificity of FDG-PET for AD pathology, the addition of amyloid PET tracers may be useful for increasing the specificity of the diagnosis. Since emerging evidence suggests that distinct patterns of change may be detected in each modality for patients and people at risk, an effective strategy may be to combine dementia-sensitive FDG-PET with pathology-specific Aβ measures. For example, an abnormal FDG-PET scan may be used to determine either the presence or risk of a dementing disorder, and an additional positive finding on FDG-PiB may successfully distinguish AD from DLB, which typically has much less amyloid deposition and therefore is more likely to show a negative PiB scan. Additional validation studies are needed before Aβ PET imaging can enter into clinical practice, and more longitudinal studies are necessary to establish the limits and strengths of both tracers for early diagnosis of AD. We anticipate that continued technological progress will one day allow us to image all aspects of AD pathology in vivo, at proper microscopic resolution, at the earliest possible stages of disease.

Figure 3
“Food for thought”:coregistered PIB- and FDG-PET scans in 4 representative clinically and cognitively normal individuals: A) Negative PIB and normal FDG uptake. This individual is at conceivably low risk for AD; B) positive PIB and abnormal ...


Fundings.—This study was supported by NIH/NIA grants AG35137, AG032554 and AG13616, NIH/NCRR grant M01-RR0096, the Alzheimer’s Association, and an Anonymous foundation.


1. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer‘s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer‘s Disease. Neurology. 1984;34:939–944. [PubMed]
2. Kukull WA, Higdon R, Bowen JD, McCormick WC, Teri L, Schellenberg GD, et al. Dementia and Alzheimer disease incidence: A prospective cohort study. Arch Neurol. 2002;59:1737–1746. [PubMed]
3. Brookmeyer R, Johnson E, Ziegler-Graha K, Arrighi HM. Forecasting the global burden of Alzheimer’s disease. Alzh Dementia. 2007;3:186–191. [PubMed]
4. Hebert LE, Scherr PA, Bienias JL, Bennett DA, Evans DA. State-specific projections through 2025 of alzheimer disease prevalence. Neurology. 2004;62:1645. [PubMed]
5. Hampel H, Frank R, Broich K, Teipel SJ, Katz RG, Hardy J, et al. Biomarkers for Alzheimer’s disease: academic, industry and regulatory perspectives. Nat Rev Drug Discov. 2010;9:560–574. [PubMed]
6. Johnson DK, Storandt M, Morris JC, Galvin JE. Longitudinal study of the transition from healthy aging to alzheimer disease. Arch Neurology. 2009;66:1254–1259. [PMC free article] [PubMed]
7. Mirra SS, Heyman A, McKeel D, Sumi SM, Crain BJ, Brownlee LM, et al. The consortium to establish a registry for Alzheimer’s disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology. 1991;41:479–486. [PubMed]
8. Hebert LE, Scherr PA, Bienias JL, Bennett DA, Evans DA. Alzheimer disease in the US population: prevalence estimates using the 2000 census. Arch Neurol. 2003;60:1119–1122. [PubMed]
9. Braak H, Braak E. Development of Alzheimer-related neurofibrillary changes in the neocortex inversely recapitulates cortical myelogenesis. Acta Neuropathologica. 1996;92:197–201. [PubMed]
10. Delacourte A, David JP, Sergeant N, Buee L, Wattez A, Vermersch P, et al. The biochemical pathway of neurofibrillary degeneration in aging and Alzheimer’s disease. Neurology. 1999;52:1158–1165. [PubMed]
11. Morris JC, Storandt M, McKeel DW, Rubin EH, Price JL, Grant EA, et al. Cerebral amyloid deposition and diffuse plaques in “normal” aging: Evidence for presymptomatic and very mild Alzheiemer’s disease. Neurology. 1996;46:707–719. [PubMed]
12. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica. 1991;82:239–259. [PubMed]
13. Selkoe DJ. Alzheimer’s disease: genotypes, phenotype, and treatments. Science. 1997;275:630–631. [PubMed]
14. Lambert MP, Barlow AK, Chromy BA, Edwards C, Freed R, Liosatos M, et al. Diffusible, nonfibrillar ligands derived from Abeta1–42 are potent central nervous system neurotoxins. Proc Natl Acad Sci USA. 1998;95:6448–6453. [PMC free article] [PubMed]
15. Lue LF, Kuo YM, Roher AE, Brachova L, Shen Y, Sue L, et al. Soluble amyloid beta peptide concentration as a predictor of synaptic change in Alzheimer’s disease. Am J Pathol. 1999;155:853–862. [PMC free article] [PubMed]
16. Arriagada PV, Marzloff K, Hyman BT. Distribution of Alzheimer-type pathologic changes in nondemented elderly individuals matches the pattern in Alzheimer’s disease. Neurology. 1992;42:1681–1688. [PubMed]
17. Giannakopoulos P, Hof PR, Mottier S, Michel JP, Bouras C. Neuropathological changes in the cerebral cortex of 1258 cases from a geriatric hospital:retrospective clinicopathological evaluation of a 10-year autopsy population. Acta Neuropathologica. 1994;87:456–468. [PubMed]
18. Ulrich J. Alzheimer changes in nondemented patients younger than sixty-five:Possible early stages of Alzheimer’s disease and senile dementia of Alzheimer type. Ann Neurol. 1985;17:273–277. [PubMed]
19. Morrison JH, Hof PR. Life and death of neurons in the aging brain. Science. 1997;278:412–419. [PubMed]
20. Hyman BT, Van Hoesen GW, Damasio AR, Barnes CL. Alzheimer’s disease:cell-specific pathology isolates the hippocampal formation. Science. 1984;225:1168–1170. [PubMed]
21. Mosconi L, Berti V, Swerdlow RH, Pupi A, Duara R, de Leon MJ. Maternal transmission of Alzheimer’s disease:Prodromal metabolic phenotype and the search for genes. Human Genomics. 2010;4:170–193. [PMC free article] [PubMed]
22. Ono M, Wilson A, Nobrega J, Westaway D, Verhoeff P, Zhuang ZP, et al. 11C-labeled stilbene derivatives as Abeta-aggregate-specific PET imaging agents for Alzheimer’s disease. Nucl Med Biol. 2003;30:565–571. [PubMed]
23. Agdeppa ED, Kepe V, Liu J, Flores-Torres S, Satyamurthy N, Petric A, et al. Binding characteristics of radiofluorinated 6-dialkylamino-2-naphthylethylidene derivatives as positron emission tomography imaging probes for beta-amyloid plaques in Alzheimer’s disease. J Neurosci. 2001;21:1–5. [PubMed]
24. Rowe CC, Ackermann U, Browne W, Mulligan R, Pike KL, O’Keefe G, et al. Imaging of amyloid beta in Alzheimer’s disease with (18)F-BAY94-9172, a novel PET tracer:proof of mechanism. Lancet Neurol. 2008;7:129–135. [PubMed]
25. Klunk WE, Engler H, Nordberg A, Yanming W, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55:306–319. [PubMed]
26. Prohovnik I, Perl DP, Davis KL, Libow L, Lesser G, Haroutunian V. Dissociation of neuropathology from severity of dementia in late-onset Alzheimer disease. Neurology. 2006;66:49–55. [PubMed]
27. Neve RL, Robakis NK. Alzheimer’s disease:a re-examination of the amyloid hypothesis. Trends Neurosci. 1998;21:15–19. [PubMed]
28. Naslund J, Haroutunian V, Mohs R, Davis KL, Davies P, Greengard P, et al. Correlation between elevated levels of amyloid beta-peptide in the brain and cognitive decline. JAMA. 2000;283:1571–1577. [PubMed]
29. Dowling NM, Tomaszewski FS, Reed BR, Sonnen JA, Strauss JA, Strauss ME, et al. Neuropathological Associates of Multiple Cognitive Functions in Two Community-Based Cohorts of Older Adults. J Int Neuropsychol Soc. 2010 [In press]. [PMC free article] [PubMed]
30. Shulman JM, Chibnik LB, Aubin C, Schneider JA, Bennett DA, De Jager PL. Intermediate phenotypes identify divergent pathways to Alzheimer‘s disease. PLoS One. 2010;5:11244. [PMC free article] [PubMed]
31. Duara R, Loewenstein DA, Greig M, Acevedo A, Potter E, Appel J, et al. Reliability and validity of an algorithm for the diagnosis of normal cognition, mild cognitive impairment, and dementia:implications for multicenter research studies. Am J Geriatr Psychiatry. 2010;18:363–370. [PMC free article] [PubMed]
32. Kamat SM, Kamat AS, Grossberg GT. Dementia risk prediction: are we there yet? Clin Geriatr Med. 2010;26:113–123. [PubMed]
33. Mazziotta JC, Phelps ME. Positron Emission Tomography studies of the brain. In: Phelps ME, Mazziotta JC, Schelbert H, editors. Positron Emission Tomography & Autoradiography:Principles & Applications for the Brain & Heart. New York: Raven Press; 1986. pp. 493–579.
34. Silverman DHS, Small GW, Chang CY, Lu CS, Kung de Aburto MA, Chen W, et al. Positron emission tomography in evaluation of dementia:Regional brain metabolism and long-term outcome. JAMA. 2001;286:2120–2127. [PubMed]
35. Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol. 1997;42:85–94. [PubMed]
36. Mosconi L. Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease. Eur J Nucl Med. 2005;32:486–510. [PubMed]
37. Kim EJ, Cho SS, Jeong Y, Park KC, Kang SJ, Kang E, et al. Glucose metabolism in early onset versus late onset Alzheimer‘s disease:an SPM analysis of 120 patients. Brain. 2005;128:1790–1801. [PubMed]
38. Blass JP. Alzheimer‘s disease and Alzheimer‘s dementia:distinct but overlapping entities. Neurobiol Aging. 2002;23:1077–1084. [PubMed]
39. Grady CL, Haxby JV, Schlageter NL, Berg G, Rapoport SI. Stability of metabolic and neuropsychological asymmetries in dementia of the Alzheimer type. Neurology. 1986;36:1390–1392. [PubMed]
40. Brown AM, Sheu RK, Mohs R, Haraoutunian V, Blass JP. Correlation of the clinical severity of Alzheimer‘s disease with an aberration in mitochondrial DNA (mtDNA) J Mol Neurosci. 2001;16:41–48. [PubMed]
41. Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, Frolich L, et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage. 2002;17:302–316. [PubMed]
42. Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, et al. Multi-center standardized FDG-PET diagnosis of Mild Cognitive Impairment, Alzheimer’s disease and other dementias. J Nucl Med. 2008;49:390–398. [PMC free article] [PubMed]
43. Szelies B, Mielke R, Herholz K, Heiss W-D. Quantitative topographical EEG compared to FDG PET for classification of vascular and degenerative dementia. Electroencephalog Clin Neurophysiol. 1994;91:131–139. [PubMed]
44. Rowe CC, Ng S, Ackermann U, Gong SJ, Pike K, Savage G, et al. Imaging {beta}-amyloid burden in aging and dementia. Neurology. 2007;68:1718–1725. [PubMed]
45. Kemppainen N, Aalto S, Wilson I, Nagren K, Helin S, Bruck A, et al. Voxel-based analysis of PET amyloid ligand [11C]PIB uptake in Alzheimer disease. Neurology. 2006;67:1575–1580. [PubMed]
46. Pike KE, Savage G, Villemagne VL, Ng S, Moss SA, Maruff P, et al. Beta-amyloid imaging and memory in non-demented individuals:evidence for preclinical Alzheimer’s disease. Brain. 2007;130:2837–2844. [PubMed]
47. Mintun MAM, LaRossa GN, Sheline YIM, Dence CSM, Lee SYP, Mach RHP, et al. [11C]PIB in a nondemented population:Potential antecedent marker of Alzheimer disease. Neurology. 2006;67:446–452. [PubMed]
48. Kemppainen NM, Aalto S, Wilson IA, Nagren K, Helin S, Bruck A, et al. PET amyloid ligand [11C]PIB uptake is increased in mild cognitive impairment. Neurology. 2007;68:1603–1606. [PubMed]
49. Li Y, Rinne JO, Mosconi L, Pirraglia E, Rusinek H, De Santi S, et al. Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment and Alzheimer’s disease. Eur J Nucl Med Molec Imaging. 2008;35:2169–2181. [PMC free article] [PubMed]
50. Jack CR, Jr, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM, et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008;131:665–680. [PMC free article] [PubMed]
51. Johansson A, Savitcheva I, Forsberg A, Engler H, Langstrom B, Nordberg A, et al. [(11)C]-PIB imaging in patients with Parkinson’s disease:preliminary results. Parkins Relat Disord. 2008;14:345–347. [PubMed]
52. Johnson KA, Gregas M, Becker JA, Kinnecom C, Salat DH, Moran EK, et al. Imaging of amyloid burden and distribution in cerebral amyloid angiopathy. Ann Neurol. 2007;62:229–234. [PubMed]
53. Mathis CA, Wang Y, Klunk W. Imaging [beta]-Amyloid Plaques and Neurofibrillary Tangles in the Aging Human Brain. Current Pharmaceutical Design. 2004;10:1469–1492. [PubMed]
54. Lockhart A, Lamb JR, Osredkar T, Sue LI, Joyce JN, Ye L, et al. PIB is a non-specific imaging marker of amyloid-beta (A{beta}) peptide-related cerebral amyloidosis. Brain. 2007;130:2607–2615. [PubMed]
55. Jagust WJ. Mapping brain beta-amyloid. Curr Opin Neurol. 2009;22:356–361. [PMC free article] [PubMed]
56. Mesulam MM. Neuroplasticity failure in Alzheimer’s disease:bridging the gap between plaques and tangles. Neuron. 1999;24:521–529. [PubMed]
57. Engler H, Forsberg A, Almkvist O, Blomquist G, Larsson E, Savitcheva I, et al. Two-year follow-up of amyloid deposition in patients with Alzheimer’s disease. Brain. 2006;129:2856–2866. [PubMed]
58. Jack CR, Jr, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM, et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008;131:665–680. [PMC free article] [PubMed]
59. Klunk WE, Mathis CA, Price JC, Lopresti BJ, DeKosky ST. Two-year follow-up of amyloid deposition in patients with Alzheimer’s disease. Brain. 2006;129:2805–2807. [PubMed]
60. Jack CR, Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;8:119–128. [PMC free article] [PubMed]
61. Small GW, Kepe V, Ercoli LM, Siddarth P, Bookheimer SY, Miller KJ, et al. PET of brain amyloid and tau in mild cognitive impairment. New Engl J Med. 2006;355:2652–2663. [PubMed]
62. de Leon MJ, Convit A, Wolf OT, Tarshish CY, De Santi S, Rusinek H, et al. Prediction of cognitive decline in normal elderly subjects with 2-[18F]fluoro-2-deoxy-D-glucose/positron-emission tomography (FDG/PET) Proc Natl Acad Sci USA. 2001;98:10966–10971. [PMC free article] [PubMed]
63. Mosconi L, De Santi S, Li J, Tsui WH, Li Y, Boppana M, et al. Hippocampal hypometabolism predicts cognitive decline from normal aging. Neurobiol Aging. 2008;29:676–692. [PMC free article] [PubMed]
64. Jagust WJ, Gitcho A, Sun F, Kuczynski B, Mungas D, Haan M. Brain imaging evidence of preclinical Alzheimer’s disease in normal aging. Ann Neurol. 2006;59:673–681. [PubMed]
65. Mosconi L, Mistur R, Switalski R, Tsui W, Glodzik L, Li Y, et al. FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer’s disease. Eur J Nucl Med Molec Imaging. 2009;36:811–822. [PMC free article] [PubMed]
66. Morris JC, Roe CM, Grant EA, Head D, Storandt M, Goate AM, et al. Pittsburgh compound B imaging and prediction of progression from cognitive normality to symptomatic Alzheimer disease. Arch Neurol. 2009;66:1469–1475. [PMC free article] [PubMed]
67. Jobst KA, Barnetson LPD, Shepstone BJ. Accurate prediction of histologically confirmed Alzheimer’s disease and the differential diagnosis of dementia: The use of NINCDS-ADRDA and DSM-III-R criteria, SPECT, X-ray CT and Apo E4 in medial temporal lobe dementia. Int Psychogeriat. 1998;10:271–302. [PubMed]
68. Tanzi RE, Bertram L. New frontiers in Alzheimer’s disease genetics. Neuron. 2001;32:181–184. [PubMed]
69. Kennedy AM, Newman SK, Frackowiak RS, Cunningham VJ, Roques P, Stevens J, et al. Chromosome 14 linked familial Alzheimer’s disease. A clinico-pathological study of a single pedigree. Brain. 1995;118:185–205. [PubMed]
70. Perani D, Grassi F, Sorbi S, Nacmias B, Piacentini S, Piersanti P, et al. PET study in subjects from two Italian FAD families with APP717 Val to llue mutation. Eur J Neurol. 1997;4:214–220.
71. Kennedy AM, Frackowiak RSJ, Newman SK, Bloomfield PM, Seaward J, Roques P, et al. Deficits in cerebral glucose metabolism demonstrated by positron emission tomography in individuals at risk of familial Alzheimer’s Disease. Neurosci Lett. 1995;186:17–20. [PubMed]
72. Mosconi L, Sorbi S, de Leon MJ, Li Y, Nacmias B, Bessi V, et al. Hypometabolism exceeds atrophy in presymptomatic early-onset Familial Alzheimer’s disease. J Nucl Med. 2006;47:1778–1786. [PubMed]
73. Klunk WE, Price JC, Mathis CA, Tsopelas ND, Lopresti BJ, Ziolko SK, et al. Amyloid deposition begins in the striatum of presenilin-1 mutation carriers from two unrelated pedigrees. J Neurosci. 2007;27:6174–6184. [PMC free article] [PubMed]
74. Koivunen J, Verkkoniemi S, Aalto S, Paetau A, Ahonen JP, Viitanen M, et al. PET amyloid ligand [11C]PIB uptake shows predominantly striatal increase in variant Alzheimer’s disease. Brain. 2008;131:1845–1853. [PubMed]
75. Remes AM, Laru L, Tuominen H, Aalto S, Kemppainen N, Mononen H, et al. Carbon 11-Labeled Pittsburgh Compound B Positron Emission Tomographic Amyloid Imaging in Patients With APP Locus Duplication. Arch Neurol. 2008;65:540–544. [PubMed]
76. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment:clinical characterization and outcome. Arch Neurol. 1999;56:303–308. [PubMed]
77. Chetelat G, Desgranges B, De La Sayette V, Viader F, Eustache F, Baron JC. Mild cognitive impairment:Can FDG-PET predict who is to rapidly convert to Alzheimer‘s disease? Neurology. 2003;60:1374–1377. [PubMed]
78. Mosconi L, Perani D, Sorbi S, Herholz K, Nacmias B, Holthoff V, et al. MCI conversion to dementia and the APOE genotype:a prediction study with FDG-PET. Neurology. 2004;63:2332–2340. [PubMed]
79. Drzezga A, Lautenschlager N, Siebner H, Riemenschneider M, Willoch F, Minoshima S, et al. Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer‘s disease:a PET follow-up study. Eur J Nucl Med Molec Imaging. 2003;30:1104–1113. [PubMed]
80. Silverman DHS, Truong CT, Kim SK, Chang CY, Chen W, Kowell AP, et al. Prognostic value of regional cerebral metabolism in patients undergoing dementia evaluation:comparison to a quantifying parameter of subsequent cognitive performance and to prognostic assessment without PET. Mol Genet Metab. 2003;80:350–355. [PubMed]
81. Herholz K, Nordberg A, Salmon E, Perani D, Kessler J, Mielke R, et al. Impairment of neocortical metabolism predicts progression in Alzheimer’s disease. Dement Geriatr Cog Dis. 1999;10:494–504. [PubMed]
82. Drzezga A, Grimmer T, Riemenschneider M, Lautenschlager N, Siebner H, Alexopoulus P, et al. Prediction of individual outcome in MCI by means of genetic assessment and 18F-FDG PET. J Nucl Med. 2005;46:1625–1632. [PubMed]
83. Anchisi D, Borroni B, Franceschi M, Kerrouche N, Kalbe E, Beuthien-Beumann B, et al. Heterogeneity of brain glucose metaboism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch Neurol. 2005;62:1728–1733. [PubMed]
84. Lowe VJ, Kemp BJ, Jack CR, Jr, Senjem M, Weigand S, Shiung M, et al. Comparison of 18F-FDG and PiB PET in cognitive impairment. J Nucl Med. 2009;50:878–886. [PMC free article] [PubMed]
85. Forsberg A, Engler H, Almkvist O, Blomqvist G, Hagman G, Wall A, et al. PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging. 2008;29:1456–1465. [PubMed]
86. Okello A, Koivunen J, Edison P, Archer HA, Turkheimer FE, Nagren K, et al. Conversion of amyloid positive and negative MCI to AD over 3 years. An 11C-PIB PET study. Neurology. 2009;73:754–760. [PMC free article] [PubMed]
87. Wolk DA, Price JC, Saxton JA, snitz BE, James JA, Lopez OL, et al. Amyloid imaging in mild cognitive impairment subtypes. Ann Neurol. 2009;65:557–568. [PMC free article] [PubMed]
88. Laws SM, Hone E, Gandy S, Martins RN. Expanding the association between the APOE gene and the risk of Alzheimer’s disease:possible roles for APOE promoter polymorphisms and alterations in APOE transcription. J Neurochem. 2003;84:1215–1236. [PubMed]
89. Small GW, Mazziotta JC, Collins MT, Baxter LR, Phelps ME, Mandelkern MA, et al. Apolipoprotein E type 4 allele and cerebral glucose metabolism in relatives at risk for familial Alzheimer disease. JAMA. 1995;273:942–947. [PubMed]
90. Reiman EM, Caselli RJ, Yun LS, Chen K, Bandy D, Minoshima S, et al. Preclinical evidence of Alzheimer’s disease in persons homozygous for the E4 allele for apolipoprotein E. New Engl J Med. 1996;334:752–758. [PubMed]
91. Small GW, Ercoli LM, Silverman DHS, Huang SC, Komo S, Bookheimer S, et al. Cerebral metabolic and cognitive decline in persons at genetic risk for Alzheimer’s disease. Proc Natl Acad Sci USA. 2000;97:6037–6042. [PMC free article] [PubMed]
92. Reiman EM, Caselli RJ, Chen K, Alexander GE, Bandy D, Frost J. Declining brain activity in cognitively normal apolipoprotein E epsilon 4 heterozygotes:A foundation for using positron emission tomography to efficiently test treatments to prevent Alzheimer’s disease. Proc Natl Acad Sci USA. 2001;98:3334–3339. [PMC free article] [PubMed]
93. Reiman EM, Chen K, Alexander GE, Caselli RJ, Bandy D, Osborne D, et al. Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proc Natl Acad Sci USA. 2004;101:284–289. [PMC free article] [PubMed]
94. Reiman EM, Chen K, Liu X, Bandy D, Yu M, Lee W, et al. Fibrillar amyloid-beta burden in cognitively normal people at 3 levels of genetic risk for Alzheimer’s disease. Proc Natl Acad Sci USA. 2009;106:6820–6825. [PMC free article] [PubMed]
95. Cupples LA, Farrer LA, Sadovnik AD, Relkin N, Whitehouse P, Green P. Estimating risk curves for first-degree relatives of patients with Alzheimer’s disease:The REVEAL study. Genet Med. 2004;6:192–196. [PubMed]
96. Green RC, Cupples LA, Go R, Benke KS, Edeki T, Griffith PA, et al. Risk of dementia among white and African American relatives of patients with Alzheimer disease. JAMA. 2002;287:329–336. [PubMed]
97. Silverman JM, Ciresi G, Smith CJ, Marin DB, Schnaider-Beeri M. Variability of familial risk of Alzheimer disease across the late life span. Arch Gen Psychiat. 2005;62:565–573. [PubMed]
98. Mosconi L, Brys M, Switalski R, Mistur R, Glodzik-Sobanska L, Pirraglia E, et al. Maternal family history of Alzheimer’s disease predisposes to reduced brain glucose metabolism. Proc Natl Acad Sci USA. 2007;104:19067–19072. [PMC free article] [PubMed]
99. Mosconi L, Mistur R, Glodzik L, Brys M, Switalski R, Pirraglia E, et al. Declining brain glucose metabolism in normal individuals with a maternal history of Alzheimer’s. Neurology. 2009;72:513–520. [PMC free article] [PubMed]
100. Mosconi L, Rinne JO, Tsui W, Berti V, Li Y, Murray J, et al. Increased fibrillar amyloid-β burden in normal individuals with a family history of late-onset Alzheimer’s disease. Proc Natl Acad Sci USA. 2010;107:5949–5954. [PMC free article] [PubMed]
101. Honea RA, Swerdlow RH, Vidoni E, Goodwin J, Burns JM. Reduced gray matter volume in normal adults with a maternal family history of Alzheimer disease. Neurology. 2009;74:113–120. [PMC free article] [PubMed]
102. Andrawis JP, Hwang KS, Green AE, Kotlerman J, Elashoff D, Morra JH, et al. Effects of ApoE4 and maternal history of dementia on hippocampal atrophy. Neurobiol Aging. 2010 [In press]. [PMC free article] [PubMed]
103. Bendlin BB, Ries ML, Canu E, Sodhi A, Lazar M, Alexander AL, et al. White matter is altered with parental family history of Alzheimer’s disease. Alzheimers Dement. 2010;6:394–403. [PMC free article] [PubMed]
104. Crystal HA, Dickson D, Davies P, Masur D, Grober E, Lipton RB. The relative frequency of “dementia of unknown etiology” increases with age and is nearly 50% in nonogenerians. Arch Neurol. 2000;57:713–719. [PubMed]
105. Crystal H, Dickson D, Fuld P, Masur D, Scott R, Mehler M, et al. Clinico-pathologic studies in dementia:nondemented subjects with pathologically confirmed Alzheimer’s disease. Neurology. 1988;38:1682–1687. [PubMed]
106. Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, et al. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58:1985–1992. [PubMed]
107. Price JL, Ko AI, Wade MJ, Tsou SK, McKeel DW, Morris JC. Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease. Arch Neurol. 2001;58:1395–1402. [PubMed]
108. Terry RD, Masliah E, Salmon DP, Butters N, DeTeresa R, Hill R, et al. Physical basis of cognitive alterations in Alzheimer’s disease:Synapse loss is the major correlate of cognitive impairment. Ann Neurol. 1991;30:572–580. [PubMed]
109. Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in alzheimer’s disease treatment studies. Am J Psychiatry. 2002;159:738–745. [PubMed]
110. Shoghi-Jadid K, Barrio JR, Kepe V, Huang SC. Exploring a mathematical model for the kinetics of beta-amyloid molecular imaging probes through a critical analysis of plaque pathology. Mol Imaging Biol. 2006;8:151–162. [PubMed]
111. Logan J, Fowler JS, Volkow ND, Wang GJ, Ding YS, Alexoff DL. Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab. 1996;16:834–840. [PubMed]
112. Price JC, Klunk WE, Lopresti BJ, Lu X, Hoge JA, Ziolko SK, et al. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab. 2005;25:1528–1547. [PubMed]
113. Lopresti BJ, Klunk WE, Mathis CA, Hoge JA, Ziolko SK, Lu X, et al. Simplified quantification of Pittsburgh compound B amyloid Imaging PET studies: a comparative analysis. J Nucl Med. 2005;46:1959–1972. [PubMed]
114. Shoghi-Jadid K, Barrio JR, Kepe V, Wu HM, Small GW, Phelps ME, et al. Imaging beta-amyloid fibrils in Alzheimer’s disease:a critical analysis through simulation of amyloid fibril polymerization. Nucl Med Biol. 2009;32:337–351. [PubMed]
115. Powell MR, Smith GE, Knopman DS, Parisi JE, Boeve BF, Petersen RC, et al. Cognitive Measures Predict Pathologic Alzheimer Disease. Arch Neurol. 2006;63:865–868. [PubMed]
116. Masters CL, Simms G, Weinman NA, Multhaup G, McDonald B, Beyreuther K. Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc Natl Acad Sci USA. 1985;82:4245–4249. [PMC free article] [PubMed]
117. Isacson O, Seo H, Lin L, Albeck D, Granholm AC. Alzheimer’s disease and Down’s syndrome:roles of APP, trophic factors and ACh. Trends Neurosci. 2002;25:79–84. [PubMed]
118. Price JL, Morris JC. Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol. 1999;45:358–368. [PubMed]
119. Davies L, Wolska B, Hilbich C, Multhaup G, Martins R, Simms G, et al. A4 amyloid protein deposition and the diagnosis of Alzheimer’s disease:prevalence in aged brains determined by immunocytochemistry compared with conventional neuropathologic techniques. Neurology. 1988;38:1688–1693. [PubMed]
120. Silverman DHS, Small GW, Phelps ME. Clinical value of neuroimaging in the diagnosis of dementia. sensitivity and specificity of regional cerebral metabolic and other parameters for early identification of Alzheimer’s disease. Clin Positron Imaging. 1999;2:119–130. [PubMed]
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