Genetic and degenerative disorders primarily causing dementia

Handbook of Clinical Neurology, Vol. 135 (3rd series) Neuroimaging, Part I J.C. Masdeu and R.G. Gonza´lez, Editors © 2016 Elsevier B.V. All rights reserved

Chapter 26

Genetic and degenerative disorders primarily causing dementia JOSEPH C. MASDEU* AND BELEN PASCUAL Department of Neurology, Houston Methodist Hospital, Houston, TX, USA

Abstract Neuroimaging comprises a powerful set of instruments to diagnose the different causes of dementia, clarify their neurobiology, and monitor their treatment. Magnetic resonance imaging (MRI) depicts volume changes with neurodegeneration and inflammation, as well as abnormalities in functional and structural connectivity. MRI arterial spin labeling allows for the quantification of regional cerebral blood flow, characteristically altered in Alzheimer’s disease, diffuse Lewy-body disease, and the frontotemporal dementias. Positron emission tomography allows for the determination of regional metabolism, with similar abnormalities as flow, and for the measurement of b-amyloid and abnormal tau deposition in the brain, as well as regional inflammation. These instruments allow for the quantification in vivo of most of the pathologic features observed in disorders causing dementia. Importantly, they allow for the longitudinal study of these abnormalities, having revealed, for instance, that the deposition of b-amyloid in the brain can antecede by decades the onset of dementia. Thus, a therapeutic window has been opened and the efficacy of immunotherapies directed at removing b-amyloid from the brain of asymptomatic individuals is currently being tested. Tau and inflammation imaging, still in their infancy, combined with genomics, should provide powerful insights into these disorders and facilitate their treatment.

INTRODUCTION1 Dementia has been defined as an acquired loss of intellectual abilities of sufficient severity to interfere with social or occupational functioning (American Psychiatric Association, 1994). The somewhat pejorative term “dementia,” still very useful in scientific writing and widely used by the public, is being replaced in the psychiatric literature by the less threatening “major neurocognitive disorder” (American Psychiatric Association, 2013). More relevant to the scope of this chapter, dementia refers to cognitive deterioration due to diffuse or disseminated disease of the cerebral hemispheres (Petersen, 2004). Bilateral hemispheric lesions identified by imaging can belong to any of a number of categories reviewed in this volume: vascular, autoimmune, infectious, traumatic,

metabolic, or even neoplastic. This chapter focuses on the most frequent cause of dementia, namely neurodegenerative disorders. The landscape of imaging of neurodegenerative disorders causing dementia is changing rapidly. Until the 2012 approval by the Food and Drug Administration of florbetapir to image brain b-amyloid (abeta) deposition (Johnson et al., 2013b), neuroimaging in patients with slowly progressive cognitive impairment leading to dementia was being used in the clinic mostly to detect tumors, hydrocephalus, or vascular lesions, all quite apparent on magnetic resonance imaging (MRI) simply by inspecting images, without the need to quantify them. Neurodegenerative disorders, however, cause subtle changes on MRI, but remarkable changes on positron

1

Abbreviations used in the chapter are listed at the end of the chapter before References Section.

*Correspondence to: Joseph C. Masdeu, M.D., Ph.D., Graham Distinguished Chair in Neurological Sciences, Houston Methodist Institute for Academic Medicine, Professor of Neurology, Weill Medical College of Cornell University, 6560 Fannin Street, Scurlock 802, Houston TX 77030, USA. Voice cell: +1-202-255-7899, E-mail: [email protected]

526

J.C. MASDEU AND B. PASCUAL

emission tomography (PET), using amyloid or metabolism markers. Now these tools are not infrequently used in the clinic to define the presence of early Alzheimer changes in patients with mild cognitive impairment (MCI) or to differentiate Alzheimer’s disease (AD) from frontotemporal dementia (FTD) (Johnson et al., 2013a; Sanchez-Juan et al., 2014). PET imaging of hyperphosphorylated tau (or, simply, tau) became of age in 2013, with the discovery of several new tau imaging agents (Villemagne et al., 2015). A better understanding of the neurobiology of dementia has also derived from the advances in neuroimaging, increasingly combined with genetic studies (Whitwell et al., 2012b). Neurodegenerative disorders are caused by genetic factors or by still incompletely understood environmental risk factors playing on a predisposing genetic background. The genetics of dementia have advanced prodigiously in the last few years, alongside the explosion in knowledge of the human genome (Lambert et al., 2013). An impressive body of work in the past 15 years, which is accelerating vertiginously at present, has led to a much clearer picture of the heterogeneity of what is still referred to as neurodegenerative dementia and of its evolution over time, as well as to the frequency of the varieties in the different age groups. Imaging findings confirm and define wellestablished neuropathologic findings in the dementias (Nelson et al., 2012). Incidence of dementia in the adult is positively correlated with age, with very few cases manifesting in people in their fourth decade of life or earlier but then increasing with each passing decade (Fig. 26.1) (Nelson et al., 2011, 2012). In the fourth and fifth decades, dementia stems from cerebrovascular disease, particularly of the microvascular type, from frontotemporal processes, from diffuse Lewy-body disease (DLB), and from autosomal-dominant AD, as caused by presenilin or APP mutations (Nelson et al., 2012). Later in life, AD, hippocampal sclerosis, and cerebrovascular disease are the main neuropathologically defined causes of dementia. However, more important than the changes found at the end of life are the brain abnormalities that antecede the first symptoms, because treatment at this stage may prevent or delay the clinical onset (Sperling et al., 2014). Neuroimaging has provided essential information on the preclinical evolution of AD, regularly associated with the deposition of abeta in the brain. The new insights led to the promulgation of a set of research guidelines for the diagnosis of the preclinical stages of AD (Sperling et al., 2011a). These guidelines will probably soon permeate the thinking of clinicians and eventually become commonplace in the clinic. An important environmental risk factor for neurodegeneration is traumatic brain injury (TBI): people who

Fig. 26.1. Neuropathology of dementia. Most frequent predominant neuropathologic diagnoses at each age group (Nelson et al., 2011). In each single individual a mixture of several pathologies is common.

have sustained TBI are more likely to develop dementia later in life (Barnes et al., 2014). Furthermore, even repeated mild TBI may predispose to dementia, associated with tau deposition on neuropathologic studies (McKee et al., 2013). The neurodegeneration that follows TBI may provide important information to understand the genesis of other dementias. For this reason, although TBI is discussed extensively in Chapter 22, a brief section will be devoted to it in this chapter as it is relevant for a type of dementia, chronic traumatic encephalopathy (CTE). Similarly, vascular brain disease has been described in Chapters 16–18. However, aspects of vascular disease that specifically relate to dementia will be discussed briefly here. The characteristic MRI findings of Creutzfeldt–Jakob disease, a rare cause of dementia, are discussed in Chapter 19.

CHRONIC TRAUMATIC ENCEPHALOPATHY Cognitive impairment has been described after repeated mild TBI (McKee et al., 2013; Barnes et al., 2014). It may be progressive in the absence of additional traumatic events, and therefore an ongoing neurodegenerative process is likely (Fig. 26.2). Clinically, the patients may have a syndrome resembling Parkinson disease with dementia, such as the typical dementia pugilistica of boxers, or a dementia with executive function loss, more typically seen with war or football injuries (Stern et al., 2013).

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

527

Fig. 26.2. Cellular events after mild traumatic brain injury. Acceleration–deceleration forces would injure neurons, inducing abnormal tau deposition within them. Either while injured neurons are still viable or after neuronal death, tau would be presented to microglia, setting up an inflammatory cascade involving peripheral T cells and the production of cytokines, which would cause oxidative stress not only to the damaged neurons, inducing their death, but also involving intact nearby neurons. Furthermore, tau could propagate from damaged to intact neurons, causing disease progression, independent from additional traumatic events. Similar events, with a trigger other than trauma, could be postulated in other neurodegenerative brain disorders. HLA-DR, human leukocyte antigen (class II); IL-1b, interleukin 1 beta; NO, nitrous oxide; ROS, reactive oxygen species; Th1, T-helper 1 immune cells; Th17, T-helper 17 immune cell; TNFa, tumor necrosis factor-alpha.

Pathologically, CTE is defined by the abnormal accumulation of tau, with a preference for the depths of the cortical sulci as compared to the gyral crests, a unique pattern that is distinct from other tauopathies, including AD (McKee et al., 2013). Using 18F-FDDNP, a tau imaging compound, increased uptake was detected in the upper brainstem, thalami, and amygdala of a group of 14 retired football players with some degree of cognitive impairment (Barrio et al., 2015). Although tau pathology has been described as the neuropathologic hallmark of CTE (McKee et al., 2013), the development of tau pathology in mild TBI has been hypothetically linked to both axonal damage, which can be studied with diffusion tensor imaging (DTI), and neuroinflammation (McKee et al., 2013). Supporting the role of inflammation is the presence of perivascular microgliosis in close proximity to abnormal tau in the brains of individuals with postconcussion syndromes. Brain inflammation in people after mild TBI has been documented not only neuropathologically but also using inflammation markers for PET (Folkersma et al., 2011; Ramlackhansingh et al., 2011). It is remarkable that the two available studies, both performed with a PET tracer described in the early 1990s, 11C-PK1195, were positive, because this tracer has poor brain uptake

and has yielded negative findings in other disorders with an important inflammatory component, such as AD (Groom et al., 1995; Schuitemaker et al., 2013). Second-generation inflammation markers, recently available, are much more sensitive to activated microglia. For instance, in monkey, 11C-PBR28 has an 80-fold higher specific binding than 11C-PK1195 to the protein expressed by activated microglia, an 18-kDa translocator protein (TSPO) (Kreisl et al., 2010). However, depending on a single-nucleotide polymorphism (rs6971) within the human TSPO gene, about 15% of individuals do not bind second-generation TSPO PET markers and therefore cannot be studied with these compounds (Owen et al., 2012; Kreisl et al., 2013a). Among the ones that bind them, one genotype binds about 30% less than the other and this differential binding has important effects on PET data analysis (Yoder et al., 2013), but, knowing the genotype of each study subject, it can be adequately accounted for (Kreisl et al., 2013a). At the time of this writing, there were no extant studies of second-generation PET inflammation markers in CTE, but abundant activity in this area heralds forthcoming publications. Brain metabolism, measured with 18F-2-deoxy-2fluoro-D-glucose (18F-FDG) PET, has been reported to

528

J.C. MASDEU AND B. PASCUAL

be reduced in boxers with cognitive impairment and in war veterans exposed to blast during deployment (Koerte et al., 2015). The regions most affected have not been defined, but the cerebellum has shown decreased metabolism in several studies (Koerte et al., 2015). In mild TBI or concussion, acute MRI is typically unremarkable by visual inspection. Decreased signal at the crown of a gyrus on susceptibility-weighted imaging (Fig. 26.3) would indicate a focal hemorrhage, characteristic of a contusion, and therefore would not qualify the event as a pure concussion. However, in concussion, careful comparison with control scans using susceptibility-weighted imaging may show decreased signal in the depth of the sulci, corresponding to microhemorrhages (Hahnel et al., 2008). In repetitive mild TBI, which may lead to cognitive impairment, a number of subtle MRI findings have been reported (Koerte et al., 2015), including hippocampal or frontal atrophy and white-matter abnormalities. On DTI, decreased fractional anisotropy and increased radial diffusivity have been detected in the corpus callosum and posterior limb of the internal capsule, or even more widespread in the white matter of the hemispheres (Koerte et al., 2015). Using blood oxygen level-dependent (BOLD) functional MRI (fMRI), increased connectivity of the default network and increased activation during memory or visual tasks have been detected in subjects exposed to multiple mild TBI, suggesting that these subjects need to recruit a

Fig. 26.3. Contusion. Gradient-echo magnetic resonance imaging from a 15-year-old young woman who sustained a contusion (arrow) in the inferior temporal gyrus during a soccer game.

larger area of the brain to accomplish the same task as the controls (Koerte et al., 2015).

VASCULAR COGNITIVE IMPAIRMENT Cognitive impairment to the point of dementia can result from multiple bi-hemispheric strokes (multi-infarct dementia). Bilateral ischemic lesions in Papez circuit may present as isolated memory loss (Fig. 26.4), but the sudden onset differentiates them from AD. Successive ischemic lesions in the hemispheres may mimic one of the FTDs, particularly primary progressive aphasia (PPA). While ischemic lesions do not spare the primary cortices (paracentral, auditory), neurodegenerative disorders manifesting as dementia typically do (Fig. 26.5). However, autopsy studies in a general population have shown that dementia is seldom associated with large ischemic lesions: it is much more often associated with small or even microinfarcts, related to small-artery disease (Esiri et al., 1997) and with white-matter changes on MRI (Fig. 26.6). White-matter damage is more likely when the lesions are visible on T1-weighted images; enlarged perivascular spaces can give rise to marked changes on T2 images, including FLAIR (Kirkpatrick and Hayman, 1987), but have little clinical significance. Several arteriolar disorders, discussed in Chapter 17, are characterized by dementia, such as cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) (Viswanathan et al., 2007) and cerebral amyloid angiopathy (Johnson et al., 2007; Gurol et al., 2013; Reijmer et al., 2015) (Fig. 26.7). The role of vascular versus neurodegenerative factors in the genesis of cognitive impairment in older people is currently a matter of some controversy (Chui and Ramirez-Gomez, 2015). Vascular lesions are very frequent in the brain of patients with neurodegenerative diseases, particularly in the older age groups (Schneider et al., 2007; Savva et al., 2009). These neuropathologic studies have highlighted the difficulty in attributing the cognitive impairment characteristic of dementia to either AD changes or vascular injury. Even more challenging is making a clinical differential diagnosis between vascular dementia and AD (Knopman et al., 2001; Ballard et al., 2004). The presence of white-matter changes or lacunar strokes on MRI or CT, required by the National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences (NINDS-AIREN) diagnostic criteria for vascular dementia (Roman et al., 1993), is of limited value, as patients with extensive changes on MRI may not be demented (de Leeuw et al., 2001). Some have speculated that progressive vascular dementia, as associated with small-vessel disease, is

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

529

Fig. 26.4. Critical-location-infarct dementia. This 74-year-old man had sustained a silent infarction of the right dorsomedialanterior nuclei of the thalamus, affecting the mammillothalamic tract of Vic D’Azyr. He became symptomatic, with loss of episodic memory for recent events and impaired executive function, after the second infarction, almost symmetric to the first, on the left side. The chronic infarctions (arrows) are more apparent on computed tomography (CT) than on fluid-attenuated inversion recovery (FLAIR) imaging. Note the arteriosclerotic changes in the proximal segment of the posterior cerebral artery (arrow). MRA, magnetic resonance angiography.

simply vascular brain disease plus AD (Nolan et al., 1998). By contrast, AD has been postulated to be a vascular disorder (de la Torre, 2002). As both disorders increase in prevalence in the 70–90-years age group, it is difficult to separate their effects in studies showing an association between cognitive impairment and vascular disease on MRI (van der Flier et al., 2005). By contrast, using MRI as a marker of vascular disease and PET imaging of AD, the contribution of these two pathologies can be elucidated. Patients with dementia and a large vascular load on MRI may not show the characteristic metabolic pattern of AD, but show frontal and thalamic hypometabolism on 18F-FDG PET (Pascual et al., 2010) (Fig. 26.8) Likewise, vascular MRI features and amyloid deposition, the latter characteristic of AD, seem to be independent predictors of cognitive impairment (Marchant et al., 2013) and of the risk of developing cognitive decline (Vemuri et al., 2015). Vascular and neurodegenerative disorders are probably additive for the causation of dementia (Chui and Ramirez-Gomez, 2015; Vemuri et al., 2015).

ALZHEIMER DISEASE A rather long time, estimated in decades, witnesses the evolution of neurobiologic events leading to, but preceding, the cognitive impairment characteristic of AD (Jack et al., 2013; Villemagne et al., 2013). In autosomaldominant AD, where the timing of the onset of dementia can be predicted with a certain accuracy, cerebrospinal

fluid (CSF) Ab1–42 declined 25 years before onset, followed by amyloid deposition as measured by PET imaging 15 years before onset, along with increased CSF tau and hippocampal atrophy. This was followed by cerebral hypometabolism on 18F-FDG PET about 10 years before onset (Bateman et al., 2012). A similar sequence seems to be present with abeta deposition in sporadic, late-onset AD, although the etiologic mix in the more advanced age group yields more complex biomarker results (Villemagne et al., 2013). Research AD diagnostic guidelines issued in 2011 distinguish a preclinical stage (Sperling et al., 2011a), an MCI stage (Albert et al., 2011), and an AD stage (McKhann et al., 2011). The neurobiologic cascade is likely to consist of distinct processes, still only partially understood, but the clinical progression is smooth, such that, for instance, the clinical differentiation between MCI and mild AD is far from clear-cut (Morris, 2012). Neuroimaging, with or without CSF biomarkers, is needed to detect preclinical changes (Fig. 26.9) (Sperling et al., 2011a; Jack et al., 2012; Villemagne et al., 2013; Vos et al., 2013). It also helps in the process of staging the evolution of the disorder once the patient begins to manifest cognitive impairment (Jack et al., 2010a). Recent data suggest that abeta deposition begins early in the AD process but may be one of the markers of neurodegeneration, although not the only one and perhaps not the most important (Jack et al., 2013). To study the evolution of AD, progressive atrophy from neuronal loss and loss of anisotropy in the white matter have been measured with MRI and

530

J.C. MASDEU AND B. PASCUAL

Fig. 26.5. Primary progressive aphasia, nonfluent type. An 80-year-old man with progressive impairment of language fluency for about 2 years before these images were obtained. (A) Coronal fluid-attenuated inversion recovery and axial T2-weighted magnetic resonance imaging images showing mild to moderate leukoaraiosis of the frontal periventricular white matter and lacunar infarctions in the left putamen, globus pallidus, pulvinar, and internal and extreme capsules. (B) Axial 2-deoxy-2-fluoro-D-glucose positron emission tomography (FDG-PET) showing hypometabolism in the perisylvian association cortex of the frontal, parietal, and temporal lobes of the left hemisphere. The primary auditory and motor-sensory cortices are spared. (Reproduced from Masdeu, 2008.)

fMRI respectively, synaptic dysfunction leading to an abnormal BOLD signal with fMRI (Logothetis, 2008), and to impaired metabolism with 18F-FDG (Rocher et al., 2003), and fibrillary abeta deposition in the brain with several compounds, of which 11C-PIB has been applied to the largest number of patients. Much less experience exists with specific tau markers, of which

18

F-AV-1415 is the first reported, originally as 18F-T807 (Chien et al., 2013). 18F-FDG, amyloid, and tau studies are carried out using PET. Because metabolism and brain perfusion are coupled in AD, perfusion studies with 15O water PET or single-photon emission computed tomography (SPECT) provide information similar to 18F-FDG PET (Fox et al., 1988). SPECT brain perfusion studies

Fig. 26.6. Vascular dementia. Axial T2-weighted and coronal T1-weighted images from an 80-year-old woman with impairment of cognition and gait. Note the thalamic infarctions and the large areas of altered signal (increased on T2 and decreased on T1) in the centrum semiovale. (Reproduced from Masdeu, 2008.)

Fig. 26.7. Cerebral amyloid angiopathy (CAA). Magnetic resonance images from a 72-year-old woman with dementia and CAA. The white matter contains many abnormal areas, which appear hyperintense on the transverse fluid-attenuated inversion recovery (FLAIR) image and hypointense on the sagittal T1-weighted image. Multiple lacunar infarcts are present in the lenticular nuclei and few in the thalami. Microbleeds, best seen on the gradient-echo images, dot the lenticular nuclei, thalami, and the cerebellum. Scattered microbleeds can also be seen in the cortex or subcortical white matter. (Reproduced from Masdeu, 2008.)

Fig. 26.8. Metabolism in Alzheimer’s disease (AD) and vascular dementia. From A to D: four panels, each showing in red the regions of the brain with reduced metabolism in the first, as compared to the second, of two comparison groups. In A to C each of the patient groups, AD, vascular disease and dementia (WML-D), and vascular disease without dementia (WML-nD), is compared to a healthy control group (HC). In D, WML-D is compared to WML-nD. The height thresholds for the comparisons are as follows: A, T ¼ 4.07 (voxel-level significance, p < 0.01, false discovery rate corrected); B and C, T ¼ 3.29 (voxel-level, p < 0.001); and D, T ¼ 2.12 (voxel-level, p < 0.02). In AD (A), note the classic pattern of bilaterally decreased metabolism in the parietotemporal association cortex and precuneus. The pattern in vascular disease with dementia resembles more the pattern in vascular disease without dementia than the pattern in AD. In vascular disease, patients with dementia have a reduction primarily in frontal metabolism, as compared to those who are not demented (D). Group patterns are most obvious on the superior aspect of the brain, highlighted with a frame in each panel of the figure. (Reproduced from Pascual et al., 2010.)

532

J.C. MASDEU AND B. PASCUAL 10-20 years

MCI Diagnosis

Amyloid b deposition

AD Diagnosis

Abeta PET

Tau PET

Tau deposition

Impaired synaptic function

?

FDG PET, fMRI, SPECT

Atrophy, decreased white matter anisotropy Preclinical stage

MRI, MR-DTI

MCI

AD

Evolution in time of AD neuropathobiology

Fig. 26.9. Evolution of imaging findings in Alzheimer’s disease (AD). Neurobiologic changes in the various stages in the development of AD, illustrated by specific neuroimaging techniques. A wider area in red indicates a greater degree of the neurobiologic disorder (abeta or tau deposition, impaired synaptic function or atrophy, and decreased white-matter anisotropy). Please note that the sequence of tau deposition is only an estimate, because studies are still sparse. MCI, mild cognitive impairment; amyloid PET, 11C-PIB positron emission tomography (PET); FDG PET, 18F fluoro-deoxy-glucose PET; fMRI, functional magnetic resonance imaging; SPECT, single-photon emission computed tomography; MR-DTI, diffusion tensor imaging performed with functional MRI.

are easier to perform and less expensive than PET studies (Masdeu and Arbizu, 2008). However, because they have less spatial resolution, SPECT studies have less sensitivity and specificity, particularly at early stages of the disease (Silverman, 2004; Yuan et al., 2009). More recently, arterial spin labeling has been applied to the study of brain perfusion in AD using MRI (Chen et al., 2011b). To follow the currently most conventional approach, from the older to the newer findings, we first review atrophy, followed by impaired metabolism and finally abeta deposition. However, as the disease evolves, abeta deposition antecedes the other two, and atrophy is the last to develop (Fig. 26.9). Abnormally phosphorylated tau deposition occurs in AD, but its timing in relation to abeta deposition or to the onset of clinical symptoms has not been determined by in vivo studies. Tau is closely associated with neuronal damage and cognitive impairment (Braak et al., 2011; Nelson et al., 2012; Monsell et al., 2013). Finally we will touch upon another feature of the AD brain, inflammation, now amenable to imaging.

Atrophy and white-matter anisotropy loss ATROPHY In familial autosomal-dominant AD, as caused by mutations in the presenilin and amyloid precursor protein

genes, genotyping allows for the determination, with a high degree of certainty, of who will develop the disease (Apostolova et al., 2011). Serial MRIs performed in presymptomatic individuals have suggested that atrophy in some regions, particularly the precuneus and medial temporal areas, may start as early as 4 years before the onset of cognitive impairment (Knight et al., 2011). This finding has only been determined with automated methods and has not been observed by all investigators (Ringman et al., 2010; Apostolova et al., 2011). One group has even reported increased cortical thickness in presymptomatic individuals (Fortea et al., 2010), which could suggest that an acceleration of cortical inflammatory changes before atrophy, associated with neuronal loss and increased phospho-tau in CSF, supervenes (Fortea et al., 2014). Another genetically at-risk group, cognitively intact apoliprotein E (APOE) E4 homozygotes, has been reported to have an increased rate of cortical thinning (Crivello et al., 2010) or, for APOE E4 carriers, preferential atrophy of some hippocampal subregions (Burggren et al., 2008; Mueller and Weiner, 2009; Donix et al., 2010). In cognitively normal elderly individuals, cortical thinning in precuneus and medial temporal regions has been found to correlate with subsequent cognitive decline as much as a decade later (Chiang et al., 2011; Dickerson et al., 2011; Kantarci et al., 2013) or with amyloid deposition and reduced

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA CSF abeta (Becker et al., 2011; Chetelat et al., 2012b; Dickerson and Wolk, 2012), again, suggesting that atrophy may antedate the onset of cognitive decline. The pattern of cortical thinning in people who will develop AD differs from that associated with cognitive loss of healthy aging (Bakkour et al., 2013) (Fig. 26.10). Cortical thinning associated with other AD-related changes, such as abeta deposition (Becker et al., 2011) or increased hippocampal activation during episodic memory tasks (Putcha et al., 2011), has been also been explored as a marker of early AD changes in cognitively normal older individuals. Regional atrophy correlates with regional abeta deposition, particularly in posterior cingulate cortex, in presymptomatic people or those with subjective cognitive complaints, but not in MCI or AD, suggesting that the damaging effect of abeta occurs in the presymptomatic or very mildly symptomatic stages, when abeta-

Fig. 26.10. Atrophy in healthy aging and Alzheimer’s disease (AD). Areas of the brain in which progressive atrophy is detected in healthy aging (blue), AD (red), or an overlap of either (violet). (Reproduced from Bakkour et al., 2013.)

533

reducing therapies should be applied (Chetelat et al., 2010, 2012b). In patients with MCI, thinning of the temporal cortex and precuneus is a predictor of worsening to AD, particularly when combined with neuropsychologic, PET, and CSF markers (Hua et al., 2008; Chen et al., 2011a; Heister et al., 2011; Wolz et al., 2011). Although atrophy can be appreciated visually (Fig. 26.11) (Scheltens et al., 2002; Urs et al., 2009), automated methods are more precise and facilitate longitudinal follow-up (McEvoy et al., 2009; Morra et al., 2009; Wang et al., 2009). The accuracy of software that classifies clinically appropriate cases has been compared favorably with the accuracy of trained readers (Kloppel et al., 2008). MRI end-points compared across healthy individuals and those in various stages of the AD continuum have included hippocampal volume, tensor-based morphometry, cortical thickness, and a novel technique based on manifold learning (Hua et al., 2008; Wolz et al., 2011). The best results are usually achieved combining all features, in one longitudinal clinical study yielding 67% sensitivity and 69% specificity to separate stable MCI from MCI worsening to AD, 86% and 82% to separate healthy controls from MCI worsening to AD, and 93% sensitivity and 85% specificity to separate healthy controls from AD (Wolz et al., 2011). In another longitudinal study with a 3-year follow-up (Heister et al., 2011), the combination of greater learning impairment and increased medial temporal atrophy was associated with the highest risk: 85% of patients with both risk factors converted to AD within 3 years, vs 5% of those with neither. Medial temporal atrophy was associated with the shortest median dementia-free period (Heister et al., 2011). In autopsy series, containing more advanced AD, medial temporal atrophy, even judged with a visual scale (Scheltens et al., 2002), has shown a sensitivity of 91% and specificity of 94% for autopsy-confirmed AD

Fig. 26.11. Temporal atrophy in Alzheimer’s disease (AD). Coronal magnetic resonance imaging at the level of the mammillary bodies. The entorhinal cortex has been outlined in a normal control (A) and a person with mild cognitive impairment of the amnesic type (B). Note the dilation of the temporal horns of the person with mild cognitive impairment corresponding to hippocampal atrophy. (Reproduced from Masdeu, 2008.)

534

J.C. MASDEU AND B. PASCUAL

Fig. 26.12. Atrophy in Alzheimer’s disease (AD). (A) Areas of the brain characteristically atrophied in AD. Note that they involve many of the same regions as the resting-state or default network (B). (A, Reproduced from Dickerson et al., 2009.) With permission from Oxford University Press.

(Burton et al., 2009). Regional atrophy correlates with tangle density and therefore with Braak neurofibrillary tangle stage, rather than with amyloid plaque deposition (Vemuri et al., 2008; Whitwell et al., 2008; Burton et al., 2009). Atrophy typically extends from limbic structures to neocortex at a rate of 2–5% per year (Thompson et al., 2003). The pattern of atrophy reflects preferential involvement of the parietotemporal components of the resting-state or default network (Dickerson et al., 2009) (Fig. 26.12). Atrophy shows striking network properties: when the medial temporal lobe is more atrophied, as are other regions of the ventral default network (Fig. 26.13); when the inferior parietal lobule has more

atrophy, as do other regions of the dorsal default network and frontoparietal executive systems (Dickerson et al., 2009) (Fig. 26.14). Hippocampal atrophy affects mostly the CA1 subfield, a pattern that differs from healthy aging (Chetelat et al., 2008b) (Fig. 26.15). Atrophy on MRI correlates well with the degree of regional neuronal loss (Brun and Englund, 1981). Another approach to measure neuronal loss is to quantify the regional density of neuronal receptors such as the gamma-aminobutyric acid (GABA)-A receptor, which is abundant in neurons and widespread in the brain. Binding to GABA-A receptors by 11C-flumazenil, a PET tracer, has been reported to be reduced in early AD (Pascual et al.,

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

Fig. 26.13. Ventral network degeneration in Alzheimer’s disease. Greater atrophy in the anteromedial temporal region (seed ingreen) correlates withgreater atrophy inthe temporallobes and anteromedial prefrontal cortex. (Reproduced from Dickerson et al., 2009.) With permission from Oxford University Press.

535

have been found in patients with typical AD neuropathology, including abeta deposition: typical AD (about 70% of cases), limbic-predominant AD (20%), and hippocampal-sparing AD (10%) (Whitwell et al., 2012a) (Fig. 26.17). Most patients with typical and limbic-predominant AD initially present with an amnestic syndrome, but only about 40% of those with hippocampal-sparing AD do. Medial temporal atrophy is most severe in patients with limbic-predominant AD, followed closely by typical AD, and milder in those with hippocampal-sparing AD. Conversely, the most severe cortical atrophy was noted in patients with hippocampalsparing AD, followed by those with typical disease, and then limbic-predominant AD. The ratio of hippocampal to cortical volumes allowed the best discrimination between subtypes (Whitwell et al., 2012a). In addition, some AD patients present with a profound disorder of visual perception, including one or several components of Balint’s syndrome and even field defects on confrontation testing (Rosenbloom et al., 2011; Lehmann et al., 2013a; Ossenkoppele et al., 2015). This disorder, associated with severe bilateral atrophy of the angular gyri, is called posterior cortical atrophy (Fig. 26.18).

WHITE-MATTER ANISOTROPY LOSS

Fig. 26.14. Dorsal network degeneration in Alzheimer’s disease. Greater atrophy in the inferior parietal lobule (seeds in green) correlates with greater atrophy in the precuneus and lateral temporoparietalassociation cortex. (Reproduced from Dickerson et al., 2009.) With permission from Oxford University Press.

2012a). The anatomy of reduction paralleled the distribution of neuronal loss in early AD described in neuropathologic studies (Brun and Englund, 1981) (Fig. 26.16). By correlating postmortem findings with the pattern of atrophy on MRI, three distinct atrophy patterns

White-matter abnormalities in the fornix (Ringman et al., 2007) or in fronto-occipital and inferior temporal fasciculi, the splenium of the corpus callosum, subcallosal white matter, and the cingulum bundle (Smith et al., 2010) have been found with the use of DTI in healthy individuals at risk for AD, either with autosomal-dominant mutations (Ringman et al., 2007; Fortea et al., 2010) or carrying the APOE E4 allele (Smith et al., 2010). In affected areas, DTI shows decreased functional anisotropy (Ringman et al., 2007) or increased mean diffusivity (Fortea et al., 2010). However, higher anisotropy can be found in white-matter volumes where a disrupted tract, such as the superior longitudinal fasciculus, crosses an intact one, such as the corticospinal tract, effectively increasing volume anisotropy (Douaud et al., 2011). In agreement with the anatomy of cortical atrophy, neocortical whitematter changes are more pronounced in late-myelinating fiber pathways, while sparing the corticospinal tract, originating in paracentral cortex (Stricker et al., 2009). White-matter abnormalities are also present in MCI, affecting to the greatest degree the cingulum bundle, the uncinate fasciculus, the corpus callosum, and the superior longitudinal fasciculus (Fellgiebel et al., 2004; Kiuchi et al., 2009; Mielke et al., 2009; Kantarci et al., 2010; Agosta et al., 2011; Douaud et al., 2011). Changes in the parahippocampal cingulum may separate best MCI from healthy controls (Clerx et al., 2012). For

536

J.C. MASDEU AND B. PASCUAL

Fig. 26.15. Hippocampal atrophy in Alzheimer’s disease. Areas of the hippocampus characteristically atrophied in (A) healthy aging, (B) mild cognitive impairment (MCI), and (C) Alzheimer’s disease. (Reproduced from Chetelat et al., 2008b.)

Fig. 26.16. Flumazenil positron emission tomography (PET) to detect neuronal loss in Alzheimer’s disease (AD). Areas showing neuronal loss in AD had a similar distribution in our flumazenil PET study (Pascual et al., 2012b) and in the classic histologic study of early AD by Brun and Englund (1981). In a whole-brain histologic survey of neuronal loss (A), from 0 (no neuronal loss) to 3 (most neuronal loss), the regions most affected were the temporal lobes, particularly in their medial aspect, the retrosplenial cortex and the supramarginal-angular gyri, which were the regions involved on flumazenil PET (B). (Reproduced from Pascual et al., 2012a.)

DTI, discrimination values higher than 90% have been achieved comparing MCI to healthy controls using support vector machine classifiers (O’Dwyer et al., 2012; Wee et al., 2012). However, these optimistic outcomes need to be validated in independent samples. The value of DTI to predict worsening from MCI to AD is still to be determined.

Impaired synaptic function Impaired synaptic function across the various AD stages can be gauged with techniques measuring the fMRI BOLD signal, regional metabolism, and regional perfusion. Findings are concordant, but each technique is amenable to different applications. Metabolism has been studied most extensively, but the most recent developments are the increasing use of resting-state BOLD fMRI to assess functional connectivity changes and of arterial spin labeling to measure regional perfusion using noninvasive MRI.

METABOLISM Regional cerebral metabolism studies with PET have used 18F-FDG as a metabolic marker (Jagust et al., 2007; Herholz, 2014; O’Brien et al., 2014). The most typical pattern found in early AD is decreased metabolism bilaterally in the parietotemporal association cortex and posterior cingulate gyrus (Chen et al., 2011a) (Fig. 26.19). A similar pattern has been described by one group in asymptomatic, even young, APOE E4 carriers (Reiman et al., 1996, 2004). Metabolism reflects synaptic activity and therefore is most affected early in the regions to which medial temporal neurons project Miettinen et al., 2011; Bozoki et al., 2012), and may reflect impaired connectivity even in presymptomatic subjects (Drzezga et al., 2011). As atrophy corresponds to neuronal loss, it is no surprise that the regions most affected on volumetric MRI and metabolic PET do not coincide early in the disease (Chetelat et al., 2008a), but they partially overlap as the disease progresses (La Joie et al., 2012)

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

537

Fig. 26.17. Imaging and pathology types of Alzheimer’s disease (AD). On standard templates, regions of atrophy on magnetic resonance imaging performed during life in patients who at autopsy were found to have each of three different pathologic types of AD. The percentages indicate the frequency of each type in this sample of 177 patients. (Modified from Whitwell et al., 2012a.)

(Fig. 26.20). As the disease progresses, some areas of the frontal association cortex become hypometabolic, while the paracentral cortex (primary motor-sensory areas) remains preserved (Fig. 26.19). The specificity and sensitivity of these findings continue to be debated. In studies with neuropathologic confirmation, the sensitivity (84–95%) has been higher than the specificity (71–74%); that is, a normal study is seldom associated with AD (Silverman et al., 2001; Jagust et al., 2007). Using consensus diagnosis, in an area under the receiver operating characteristic (ROC) analysis for three automated approaches to mild AD diagnosis, the specificity approximates 85% when the sensitivity is pegged at 80% (Caroli et al., 2012). Depending on the approach and the sample studied, the accuracy for predicting the evolution of MCI to AD varies from 0.774 to 0.983 (Caroli et al., 2012). Among persons with MCI, those most likely to progress to AD have metabolic findings similar to AD (Mosconi et al., 2007). 18F-FDG PET may predict better than structural MRI or SPECT the worsening from MCI to AD (Yuan et al., 2009).

PERFUSION Initially studied with H215O PET (Boles Ponto et al., 2006), in current clinical practice brain perfusion is most often studied with SPECT because the performance of H215O PET is both cumbersome and expensive. Arterial spin labeling could facilitate perfusion data at the same time that other MRI parameters are obtained, but it requires further validation (Chen et al., 2011b). The most commonly used tracers for studying cerebral perfusion with SPECT are Tc-99m hexamethyl propylamine oxime (Ceretec), a lipid-soluble macrocyclic amine, and Tc-99m ethyl cysteinate dimer (Neurolite). The pattern of decreased regional perfusion in parietotemporal cortex, hippocampus, anterior and posterior cingulum, and dorsomedial and anterior nucleus of the thalamus had a sensitivity of 86% and a specificity of 80% comparing AD to normal controls (Johnson et al., 1998; Masdeu et al., 2005). In a group of 70 patients with dementia and 14 controls, all with autopsy, SPECT was most useful when the clinical diagnosis was of possible AD, with a

538

J.C. MASDEU AND B. PASCUAL

Fig. 26.18. Posterior cortical atrophy in Alzheimer’s disease. (A) Conventional magnetic resonance imaging (MRI), (B) volumetric MRI on a rendered image, and (C) positron emission tomography (PET) studies from a 61-year-old woman with a 3-year history of progressive reading difficulties, agraphia, and dressing apraxia. On examination she had a Balint syndrome with simultanagnosia, apraxia of eye movements, optic ataxia, and “tunnel vision.” Note in (A) the marked atrophy in the lateral parietal lobe, with dilation of the intraparietal sulcus (arrows). There is also hippocampal atrophy, albeit less prominent. (B) Voxels with significant atrophy (compared with statistical parametric mapping to 48 controls, p < 0.05 uncorrected; k > 20) are displayed in red. Note that in (B) the right side of the brain is displayed on the right side of the image, opposite to the radiologic convention on the conventional MRI (A) and PET (C). Areas of decreased metabolism on PET (C) most closely match the clinical picture.

probability of a diagnosis of AD of 67% without SPECT, of 84% with a positive SPECT, and of 52% with a negative SPECT (Jagust et al., 2001). However, to predict progression from MCI to AD, SPECT has been reported to have 41.9% sensitivity and 82.3% specificity (Devanand et al., 2010b), although a meta-analysis assigned to SPECT a similar predictive value as MRI measurements (Yuan et al., 2009). A head-to-head comparison of perfusion SPECT with metabolism PET has shown a much better

sensitivity and specificity of PET over SPECT in AD and DLB (O’Brien et al., 2014).

BOLD SIGNAL Activation The great variety of methods, including different activation paradigms, has yielded disparate results among various groups. For instance, both increased

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

539

discrepancy in activation in APOE E4 carriers may be explained by their failure to experience a decrease in activation with age, as other APOE genotypes do (Nichols et al., 2012). Then, APOE E4 carriers will be likely to show decreased activation compared to other APOE genotypes in young samples (Johnson et al., 2006; Mondadori et al., 2007), but increased activation in older samples (Bookheimer et al., 2000; Bondi et al., 2005; Fleisher et al., 2005; Trivedi et al., 2008). Despite the complexity of activation results, a bimodal pattern seems consistent. Medial temporal activation, increased in asymptomatic at-risk subjects (Quiroz et al., 2010; Putcha et al., 2011; Braskie et al., 2012; but see Ringman et al., 2011), tends to decrease as the AD process worsens and cognition deteriorates (Dickerson et al., 2004; O’Brien et al., 2010). Indeed, increased activation in mildly symptomatic, or even asymptomatic, individuals may predict their worsening (Dickerson et al., 2004; O’Brien et al., 2010). Functional connectivity

Fig. 26.19. Deoxy-2-fluoro-D-glucose positron emission tomography (FDG PET) group findings in Alzheimer’s disease. Projected on a rendered magnetic resonance imaging and shown in red are areas with low metabolism in a group of 28 patients with early Alzheimer’s disease, compared with 28 healthy controls. Note sparing of the paracentral (primary motor-sensory) cortex. (Reproduced from Masdeu, 2008.)

Fig. 26.20. Areas of greatest atrophy and lowest metabolism in Alzheimer’s disease. Projected on surface templates are areas of the brain with greatest atrophy (in blue) and greatest metabolic loss (in red). (Courtesy of Dr. Renaud La Joie, Institut National de la Sante´ et de la Recherche Me´dicale (Inserm), Unite´, 1077 Caen, France.)

(Bondi et al., 2005; Bookheimer et al., 2000; Fleisher et al., 2005; Trivedi et al., 2008) and decreased (Johnson et al., 2006; Mondadori et al., 2007) medial temporal-lobe activation has been reported in APOE E4 carriers. Some apparent differences may reflect still unclear underlying biology. For instance, the apparent

Abnormalities in functional connectivity have been found consistently in the different stages of AD and correspond to abnormal DTI, volumetric and metabolic findings (Sperling et al., 2010; Filippi and Agosta, 2011). Most of the recent studies have explored resting BOLD, easier and faster to obtain than activation paradigms. This potentially powerful technique depends heavily on careful data recording and analysis; even in the best hands it can yield results that reflect nonbiologic variables, such as greater movement in the scanner on the part of one of the study groups (Power et al., 2012). As with other neuroimaging findings, abnormal connectivity may already be detected in presymptomatic, at-risk individuals, particularly to and from areas, like the posterior cingulate gyrus, precuneus, and medial temporal regions, known to be affected early in the disease (Hedden et al., 2009; Dennis et al., 2010; Sheline et al., 2010; Gour et al., 2011; Machulda et al., 2011; Jacobs et al., 2012). Unlike atrophy, impaired functional connectivity reflects synaptic dysfunction, not neuronal loss. For this reason it tends to be affected in areas with low metabolism (Drzezga et al., 2011) and amyloid deposition (Hedden et al., 2009; Drzezga et al., 2011). Different precuneus connectivity patterns have been reported in AD and DLB (Galvin et al., 2011; Kenny et al., 2012) and across APOE genotypes (Sheline et al., 2010).

ABETA DEPOSITION Undoubtedly, abeta imaging, accomplished for the first time in humans in February 2002 (Kadir et al., 2011), has been the greatest boon yet for early-stage AD imaging (Fig. 26.21). Brain abeta has been imaged most

540

J.C. MASDEU AND B. PASCUAL

Fig. 26.21. Pittsburgh compound B (11C-PIB)-positive scan in Alzheimer’s disease (AD). In yellow, areas of the brain with abeta deposition, displayed on the magnetic resonance imaging of this 65-year-old woman. Note the distribution of amyloid binding, predominantly in the frontal and temporal lobes, as well as precuneus. There is slight binding to white matter, but not as pronounced as in the 18F-florbetapir scan (Fig. 26.22).

extensively with “Pittsburgh compound B” (11C-PIB) (Rowe and Villemagne, 2011), helping separate the dementias with marked abeta deposition from the rest (Table 26.1). PIB is only available bound to 11C, a positron-emitting isotope with a half-life of 20.4 minutes, but since 2012 there are abeta-imaging compounds bound to 18F, with a half-life of 109.8 minutes (Table 26.2). The longer half-life allows for the radiotracer to be synthesized at a facility with a cyclotron and then shipped to institutions with PET cameras, a process much less expensive than having an on-site cyclotron and one that is also potentially available at many health care settings. Good concordance with histologically measured abeta load has been shown for three PET abeta binding agents, 18F-florbetapir (Choi et al., 2012), 18F-flutemetamol (Wolk et al., 2011), and 18F-florbetaben (Sabri et al., 2015). All of them are approved by the Food and Drug Administration for use in the clinical setting. Newer 18F compounds are on the way (Rowe et al., 2013), attempting to correct the marked nonspecific white-matter binding of currently available 18F abeta compounds. In early AD, 11C-PIB binds mostly to frontoparietotemporal association cortex, sparing the paracentral regions and primary sensory cortex. It also binds to striatum. The regional retention of PIB-like compounds reflects the regional density of abeta plaques (Bacskai et al., 2007; Ikonomovic et al., 2008; Kadir et al., 2011; Choi et al., 2012). Like another biomarker of AD, decreased CSF abeta 42 (Jack et al., 2011), abeta brain deposition begins in the preclinical stages of AD, increases during the MCI stage and, by the time of the AD diagnosis, remains relatively stable as the disease progresses (Fig. 26.9) (Jack et al., 2008; Villemagne et al., 2011b; Ossenkoppele et al., 2012). Thus, it is a marker of the preclinical stages of the disease and correlates with the degree of cognitive impairment only in the preclinical stages and MCI, not

during AD (Koivunen et al., 2011; Villemagne et al., 2011b; Chetelat et al., 2012a; Perrotin et al., 2012), while atrophy and synaptic dysfunction continue to increase and spread as clinical AD worsens and cognition deteriorates (Jack et al., 2008; Mormino et al., 2009; Devanand et al., 2010a; Koivunen et al., 2011). In asymptomatic individuals of a similar age, abeta deposition has been found more often among APOE4 carriers (Castellano et al., 2011; Vlassenko et al., 2011). Lifetime cognitive engagement has been found to protect from preclinical abeta deposition in some studies (Landau et al., 2012) but not in others (Vemuri et al., 2012). Also in asymptomatic individuals, a more sedentary lifestyle has been associated with higher abeta levels, but only among APOE4 carriers (Head et al., 2012). Furthermore, longitudinal imaging allows for the evaluation of the natural history of abeta deposition among at-risk genotypes (Vlassenko et al., 2011), and it has the potential to be a marker of effectiveness in studies carried out during the preclinical stage of AD, as it has helped elucidate brain changes during AD therapy (Sperling et al., 2012). Although more data are needed, abeta deposition could be the strongest and earliest neuroimaging predictor of worsening to AD. In a 2-year follow-up of MCI patients, 19 of 30 11C-PIB-positive patients worsened to AD, whereas only one among the 11C-PIB-negative patients did (Okello et al., 2009b; Wolk et al., 2009). Among the 11C-PIB-negative MCI patients, 3 regained normal cognition and others developed non-AD dementias (Okello et al., 2009b; Wolk et al., 2009), providing a specificity for AD possibly superior to other imaging biomarkers at a very early stage of the AD spectrum. In addition, 11C-PIB deposition predicts time to conversion to AD (Jack et al., 2010b). Another PET amyloid marker, 18F-FDDNP, has also been reported to predict worsening from MCI to AD (Small et al., 2012; but see Ossenkoppele et al., 2012).

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

541

Table 26.1 Clinical, imaging, and genetic findings associated with the neurodegenerative dementias

Dementia type Clinical findings AD

Predisposing gene variants or mutations

+

+

+> > 

+

nfvPPA

Nonfluent speech, agrammatism



+>

svPPA

Anomic aphasia, loss of comprehension, surface dyslexia Behavioral and personality changes, executive dysfunction

> > +

 > > + GRN, MAPT, C9orf72



+

MAPT, GRN, C9orf72, FUS, CHMP2B



+

MAPT, GRN



+

MAPT

Similar to AD, but less Posterior parieto- + medial temporal atrophy occipital association cortex, putamen

+

APOE4, GBA

bvFTD

CBD

Apraxia, rigidity

PSP

Supranuclear palsy, executive function loss, parkinsonism Memory loss, visual hallucinations, parkinsonism

DLB

Precuneus, lateral parietotemporal association cortex Left posterior perisylvian or parietal association cortex Left posterior frontoinsular Left posterior association cortex frontoinsular association cortex Left or right anterior Left or right temporal lobe anterior temporal lobe Symmetric to moderately Anterior frontal and temporal right predominant frontal cortex or anterior temporal association regions cortex Superior parietal lobule Superior parietal lobule, premotor cortex, putamen Midbrain Frontal association cortex

b-amyloid Tau (PET) (PET)

Medial temporal, precuneus, lateral temporoparietal association cortex Left posterior perisylvian Impaired repetition of or parietal association sentences and phrases, cortex phonologic errors in speech

lvPPA

Memory loss, language, or visuospatial function impairment

Atrophy (MRI)

Decreased metabolism (PET) or perfusion (SPECT, ASL)

APOE4, TREM2, TOMM40, APP, PS1/2 APOE, TREM2, TOMM40, APP, PS1/2, MAPT MAPT, GRN

MRI, magnetic resonance imaging; PET, positron emission tomography; SPECT, single-photon emission computed tomography; ASL, arterial spin labeling; AD, Alzheimer’s disease; lvPPA, logopenic aphasia; nfvPPA, nonfluent primary progressive aphasia; svPPA, semantic variant of primary progressive aphasia; bvFTD, behavioral variant of frontotemporal dementia; CBD, corticobasal degeneration; PSP, progressive supranuclear palsy; DLB, diffuse Lewy-body dementia.

Abeta, MRI, and 18F-FDG abnormalities in healthy people with mean or median age in the decade of the 70s have been determined by two separate groups in the USA using either PET (n ¼ 430) or CSF (n ¼ 311), yielding remarkably concordant results (Knopman et al., 2012; Petersen, 2013; Vos et al., 2013). The National Institute on Aging–Alzheimer’s Association research criteria for preclinical AD were used to stage individual participants, according to results (Sperling et al., 2011a; Jack et al., 2012). About 40% of the subjects were in stage 0, without abnormal abeta or other preclinical markers; about 15% were in stage 1, with only abeta abnormality;

about 12% in stage 2, with abeta plus MRI or 18F-FDG markers of AD; about 5% in stage 3, having in addition subtle cognitive decline; about 23% had MRI or 18F-FDG abnormalities characteristic of AD, but no abeta deposition (suspected non-Alzheimer pathology or SNAP); and about 5% were difficult to classify (Knopman et al., 2012; Petersen, 2013; Vos et al., 2013). These groupings had significant prognostic value, quite similar in the two studies: at 1 or 5 years, the progression rate to MCI or dementia was 2–5% for participants classed as normal, 11% for stage 1, 21–26% for stage 2, 43–56% for stage 3, and 5–10% for SNAP (Knopman et al., 2012;

542

J.C. MASDEU AND B. PASCUAL

Table 26.2 Properties of most commonly available abeta positron emission tomography (PET) tracers

11

C PIB F Florbetaben 18 F Florbetapir 18 F Flutemetamol 18 F AZD4694 (NAV4694) 18

Requires cyclotron at site

Allows for two PET studies in same visit*

Radiation exposure (in mSv)

Number of PubMed listings†

Signal-to-noise ratio

† ✓ ✓ ✓ ✓

✓

✓

✓

✓

Yes No No No No

Yes No No No No

2.93 5.8 7.00 5.92 ?

299 16 44 11 2

{

Same as PIB?

11 C PIB, Pittsburgh compound B. PET tracers are listed in the left column by publication date. Checkmarks (✓) indicate the most advantageous tracer(s) for each property (listed in the top row). *[11C] but not [18F], compounds allow for the performance of two different scans the same day. † As of December, 2014. { Regarding image quality, see Rowe et al. (2013) and Landau et al. (2014).

Vos et al., 2013). Remarkably, in the abeta PET study, the SNAP group did not differ from the groups with amyloid deposition in MRI and 18F-FDG characteristics (Knopman et al., 2013a), leading to the conclusion that these changes may be independent of abeta deposition in the brain. However, groups 2 and 3, with abnormal abeta, had a greater rate of worsening to dementia and progressive worsening of MRI and 18 F-FDG parameters, not observed in the SNAP group, in a 15-month follow-up (Knopman et al., 2013b). The proportion of abeta-negative MCI may climb to about 30% for patients aged 82 or older (Mathis et al., 2013). A few autopsies in the SNAP group have yielded inconclusive neuropathology (Vos et al., 2013). However, over a 14-year follow-up, the progression to dementia of the SNAP group is only slightly higher than that of abeta-negative, MRI-normal participants and lower than those with abeta on PET and normal MRI (Vos et al., 2013). The effect of abeta deposition on cognitive impairment in early stages of the AD continuum may be modulated by some common genetic variants. For instance, healthy APOE4 carriers not only have greater abeta deposition, but worse memory and visuospatial skills for the same amount of 11C-PIB binding (Kantarci et al., 2012a). This finding may reflect a longer period of time with abeta deposition in the APOE4 carriers. Healthy, abeta-positive carriers of the Met genotype of the brain-derived neurotrophic factor Val66Met allele have a greater worsening on follow-up in episodic memory, language, and executive function than the Val homozygotes despite similar abeta PET binding in both groups (Lim et al., 2013). Abeta imaging is also a powerful tool to separate the dementias characterized by abeta deposition, such as AD and DLB, from the FTDs, which course without abeta

deposition (Table 26.1). Separating patient samples of AD and FTD validated clinically, areas under the ROC curve for 11C-PIB (0.888) and 18F-FDG (0.910) were similar (Rabinovici et al., 2011). 11C-PIB slightly outperformed 18F-FDG in patients with known histopathology (Rabinovici et al., 2011). In this study, patients had symptoms suggestive of either disorder. More important is to define how often abeta PET is negative in patients with dementia of the AD type. In a clinical trial of early AD, 8/123 (6.5%) of APOE4 carriers and 22/61 (36.1%) of noncarriers had negative 11C-PIB studies, for a combined rate of 14% abeta-negative patients among 214 with AD symptomatology (Vellas et al., 2013). This proportion is very similar to the 14% abeta-negative in a population sample of 154 amnesic MCI patients and 16% of 58 MCI patients from the Alzheimer’s Disease Neuroimaging Initiative (Petersen et al., 2013) and may rise to 30% when the patients studied are older than 82 years (Mathis et al., 2013). It may reflect the smaller subset of patients with dementia who do not have elevated abeta or tau at autopsy, which would correspond to imaging findings about 10 years earlier (Monsell et al., 2013). Thus, these imaging findings could reflect the rather mixed pathology found in the oldest old (Nelson et al., 2011, 2012) (Fig. 26.1). However, even with a careful neuropathologic exclusion of other etiologies, clinical and neuropathologic findings are occasionally dissociated: individuals with marked abeta and neurofibrillary pathology may be cognitively intact (Monsell et al., 2013). In these individuals there is less abeta deposition in the form of fibrillar plaques and intimately related oligomeric abeta assemblies, less hyperphosphorylated soluble tau species localized in synapses, and less glial activation (Perez-Nievas et al., 2013).

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

TAU DEPOSITION In the healthy brain the protein tau stabilizes neurotubules and is therefore essential for normal neural function (Villemagne et al., 2015). However, in AD and other neurodegenerative disorders, tau becomes abnormally hyperphosphorylated, dysfunctional, and misfolded, constituting the tangles observed neuropathologically in AD and other tauopathies. The imaging compounds that we mention here do not bind to the healthy, native form of tau, but to the abnormally folded tau, using the folding properties of this protein for binding. For this reason these imaging compounds are helpful to study pathologic tau, separating it from the normal tau, which is not bound by the imaging agents and therefore not visible with them. As has become the common usage, we are referring to hyperphosphorylated tau simply as “tau.” The first compound shown to bind to tau is 18F-FDDNP (Shin et al., 2011), which binds also to abeta, but with less imaging sensitivity and specificity than the PIB-like compounds (Tolboom et al., 2010). It has shown increased binding in regions likely to have high tau, such as the medial temporal regions (Shin et al., 2010; Ercoli et al., 2012), which show relatively low 11C-PIB binding (Rowe and Villemagne, 2011). In initial stages of use in humans are several tau-binding compounds that seem to have imaging characteristics superior to FDDNP (Table 26.3). These compounds include 11CPBB3 (Maruyama et al., 2013), 18F-T807, most recently known as 18F-AV-1451 (Chien et al., 2013, 2014), and 18 F-THK5117 (Villemagne et al., 2015). 11C-PBB3 is photosensitive and therefore difficult to use in practice; it also has a high uptake in the superior sagittal sinus. The most experience exists with 18F-AV-1451, which shows highly specific uptake in areas known neuropathologically to contain a large amount of tau in AD (Ossenkoppele et al., 2015) (Fig. 26.22). It has acceptable white-matter uptake but, in older individuals, even those cognitively intact, there is uptake in the lenticular

543

nucleus, choroid plexus or its vicinity, red nucleus, and the region of the substantia nigra and subthalamic nucleus (Fig. 26.22). The reason for this uptake pattern is still unclear, but it does not seem to be related to tau deposition in these regions.

INFLAMMATION Inflammatory changes are prominent in AD: it is debated whether they are pathogenic, or simply reflect scavenging of neurons and neuronal processes, or even have a neuroprotective effect (Ferretti and Cuello, 2011; Hoozemans et al., 2011; Serrano-Pozo et al., 2011). Animal models of tau-induced neuronal loss have shown earlier and more severe inflammation than models of increased abeta (Maeda et al., 2011). However, data in humans are essential to understand the role of inflammation in the dementias. PET imaging allows in vivo quantification of neuroinflammation by measuring the density of the 18-kDa translocator protein (TSPO) in activated microglia and, to a lesser extent, in astrocytes. Activated brain microglia in AD has been largely imaged with 11C-PK11195, not an ideal compound due to its low affinity for the receptor (Kropholler et al., 2007; Okello et al., 2009a), and a low ratio of specific-tononspecific binding (Kreisl et al., 2010). However, even 11 C-PK11195 has shown moderately increased binding in AD (Cagnin et al., 2001; Schuitemaker et al., 2013) and in some patients with MCI (Okello et al., 2009a). A correlation with cognitive performance was documented in one study (Edison et al., 2008), which used also 11 C-PIB to select the AD sample. The limitations of 11 C-PK11195 have prompted the development of secondgeneration radioligands for imaging activated microglia (Chauveau et al., 2008) (Fig. 26.23 and Table 26.4). 11CPBR28 is a second-generation radioligand with high affinity to TSPO, favorable in vivo kinetics, and greater signalto-noise ratio than 11C-PK11195 in monkey brain (Kreisl et al., 2010). Unfortunately, the affinity of this and other

Table 26.3 Properties of available p-tau positron emission tomography (PET) tracers* Requires Allows for two PET cyclotron at site studies in same visit† F FDDNP ✓ F AV1451 ✓ 11 C PBB3 18 F THK5117 ✓ 18 18

No No Yes No

✓

No No Yes No

Radiation exposure (in mSv) ? 8.92 ? ?

Specificity (binds to tau, not to abeta) ✓ ✓

Poor Good Good ?

Photosensitive (requires dark-room handling) ✓ ✓ ✓

No No Yes No

PET tracers are listed in the left column by publication date. Checkmarks (✓) indicate the most advantageous tracer(s) for each property (listed in the top row). *As of May 2015. †11 C but not 18F, compounds allow for the performance of two different scans the same day.

544

J.C. MASDEU AND B. PASCUAL

Fig. 26.22. Imaging findings in a patient with Alzheimer’s disease (logopenic aphasia). Metabolism, abeta, and tau imaging from a 57-year-old woman with the logopenic-aphasia variety of Alzheimer’s disease. The primary sensory-motor areas (asterisks), as well as the primary visual (striatal cortex) and auditory (Heschl’s gyrus) regions (arrowheads), have normal metabolism and no tau deposition. By contrast, areas with high tau deposition (e.g., inferior parietal lobule, arrows) tend to have decreased metabolism. In some areas, high amyloid deposition corresponds to low metabolism and increased tau (e.g., the precuneus). However, there are areas with high amyloid load and normal metabolism, such as the medial occipital region. Uptake in the region of the substantia nigra does not correspond to tau deposition.

TSPO-binding compounds is strongly determined by the rs6971 polymorphism on the TSPO gene, leading to high- and low-affinity groups, as well as an intermediate phenotype. However, using a technique to determine binding in the intermediate phenotype, Kreisl et al. (2013b) have shown increased binding in regions typically affected in AD, particularly inferior-medial temporal regions, the inferior parietal lobule, and precuneus, but only a trend for hippocampus and precuneus in MCI. There was correlation with atrophy on MRI but not with abeta deposition when partial-volume correction was not used. Furthermore, binding correlated with several relevant cognitive measures (Kreisl et al., 2013b), suggesting

that inflammation does not precede the disorder, but accompanies progressive neuronal loss as the clinical disease progresses. Findings in AD have been less impressive with another second-generation TSPO radioligand, 11 C-DAA1106 (Yasuno et al., 2012). Increased astrocytosis has been detected in AD with 11C-DED PET (Carter et al., 2012).

IMAGING IN THE EVALUATION OF NEW AD THERAPIES Recent therapeutic trials with anti-abeta antibodies have benefited from the use of abeta imaging to determine the target effect of the medication (Sperling et al., 2011b).

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

545

cleared at least in part through the vascular system of the brain, promoting increased vascular permeability and arteriolar wall fragility. Abeta deposition in arteriolar walls, causing increased fragility, was one of the neuropathologic observations from an earlier trial using an abeta vaccine (Ferrer et al., 2004). It is possible that immunotheraphy in the preclinical stages, when amyloid levels may be lower, could minimize this untoward side effect, detectable by imaging (Fig. 26.24). As amyloid imaging has provided a powerful tool to evaluate target engagement, tau imaging may be used to evaluate therapies directed to tau spreading. Furthermore, the amount of tau load, measured with tau PET, could be used as an endpoint in trials testing the efficacy of abeta-removing therapies to arrest or slow down the deposition of abnormal tau in the brain of individuals at the presymptomatic stages of AD.

DIFFUSE LEWY-BODY DEMENTIA

Fig. 26.23. Microglial activation imaged with 11C-PBR28. From (A) a healthy control, (B) Alzheimer’s disease, and (C) frontotemporal dementia. Note that the patient with Alzheimer’s disease demonstrates diffuse increase in 11 C-PBR28 binding, whereas the frontotemporal dementia patient demonstrates increased binding localized to prefrontal cortex, where atrophy on magnetic resonance imaging is maximal. VT/fP, distribution volume corrected for plasma free fraction of radioligand. (Reproduced from Masdeu et al., 2012.)

Although improving or arresting the AD cognitive decline is the main goal of the new therapies, a more immediate need is to know whether abeta-removing therapies indeed remove abeta from the brain of the participants. Results from 11C-PIB imaging have been reported for at least two therapeutic trials of abeta-removing antibodies (Ostrowitzki et al., 2012; Sperling et al., 2012) and abeta imaging has been built into protocols targeting abeta in the presymptomatic stages of AD (Sperling et al., 2014). Combined with MRI, abeta imaging has shown that some antibodies may remove abeta from the brain and that, in regions where the original concentration of abeta was higher, focal edema and microhemorrhages are more likely to develop (Sperling et al., 2012) (Fig. 26.24). These findings suggest that abeta is

Considered as the second most common neurodegenerative dementia (Graff-Radford et al., 2014), DLB is clinically characterized by progressive, but fluctuating, cognitive impairment accompanied by visual hallucinations, parkinsonism and, in many cases, rapid eye movement sleep disorder (McKeith et al., 2005). DLB is associated with Lewy-body pathology not restricted to the substantia nigra and nucleus locus coeruleus, as in classical Parkinson’s disease (see Chapter 24), but widespread throughout the cortex. In addition to Lewy bodies, the neuropathology of DLB includes diffuse amyloid plaques (Kantarci et al., 2012b), rather than the rounded, circumscribed plaques of AD (Montine et al., 2012). Both types of plaques bind 11C-PIB (Kantarci et al., 2012b). In about half of the cases, Alzheimer-type pathology is present in the same patient, complicating the nosology of clinical DLB (Nedelska et al., 2015). Many of the imaging features of AD are also present in DLB, namely, atrophy, decreased metabolism, and abeta deposition (Rowe et al., 2007). However, unlike in AD, in pure DLB there is little medial temporal atrophy (Nedelska et al., 2015). Furthermore, compared to AD, DLB is associated with decreased occipital metabolism on 18F-FDG PET (Fig. 26.25) and with less total abeta deposition on 11C-PIB PET, although about 80% of patients have abnormal 11C-PIB PET (Kantarci et al., 2012b). In one study (Kantarci et al., 2012b), the combination of volumetry, metabolism, and abeta imaging distinguished well DLB from AD (area under the ROC ¼ 0.98). On studies of metabolism (18F-FDG PET) or perfusion (H215O PET, SPECT, arterial spin labeling), the posterior cingulate island sign is helpful to distinguish DLB from AD. Whereas the posterior cingulate gyrus, by the

546

J.C. MASDEU AND B. PASCUAL

Table 26.4 Properties of translocator protein positron emission tomography (PET) tracers used in clinical studies

Requires cyclotron at site 11

C PK1195 C PBR28 11 C DAA1106 11 C Vinpocetine 11 C DPA713 18 F DPA714 18 F FEDAA1106 18 F FEPPA 18 F PBR111 11

✓ ✓ ✓ ✓

Yes Yes Yes Yes Yes No No No No

Allows for two PET studies in same visit* ✓ ✓ ✓ ✓ ✓

Yes Yes Yes Yes Yes No No No No

Radiation exposure (in mSv)

Number of PubMed listings†

✓ ✓

✓ ✓

5.1 2.4 ? ? ? ? 36 20.2 ?

92 19 5 7 3 3 5 5 4

Signal-to-noise ratio Poor ✓ ✓ ? ? ? ? ? ?

PET tracers are listed in the left column by publication date. Checkmarks (✓) indicate the most advantageous tracer(s) for each property (listed in the top row). *11C, but not 18F, compounds allow for the performance of two different scans the same day. † As to December, 2014.

splenium of the corpus callosum, is uniformly hypometabolic or hypoperfused in AD, it is less so in DLB (Goker-Alpan et al., 2012; Graff-Radford et al., 2014) (Fig. 26.26). Dopaminergic markers, such as 18 F-FDOPA PET or 123I-beta-CIT SPECT, are likely to show decreased striatal uptake in DLB disease (Fig. 26.27), but not in AD (Lim et al., 2009). Characteristically, the decrease is greatest at the tail of the putamen and less pronounced in anterior putamen and caudate (Goker-Alpan et al., 2012) (Fig. 26.27).

FRONTOTEMPORAL DEMENTIA FTD or frontotemporal lobar degeneration is a group of diseases accounting for about 10–20% of all dementias worldwide. It affects a younger age group than AD: FTD occurs in about 3–15 per 100 000 individuals aged between 55 years and 65 years (Ferrari et al., 2014). Atrophy and white-matter abnormalities on MRI, decreased metabolism on FDG PET, and decreased perfusion on SPECT or arterial spin labeling tend to be regional and correspond well to the area preferentially affected by the pathology (Table 26.1) (Sapolsky et al., 2010; Kirshner, 2012; Zhang et al., 2013; Kerklaan et al., 2014; Agosta et al., 2015). Except for rare cases with motor neuron involvement, FTD, like AD, tends to affect association cortex, rather than primary motor or sensory cortices (Figs 26.5 and 26.18). Unlike AD, which tends to affect posterior brain regions, FDT tends to affect the anterior portion of the brain (Herholz, 2014). Hippocampal volume alone differentiates poorly AD from FTD; hippocampal sclerosis associated with FTD could explain the overlap (de Souza et al., 2013).

Frontotemporal abnormalities on FDG PET/SPECT may antedate the atrophy that eventually becomes obvious on MRI (Fig. 26.28) (Foster et al., 2007; Mendez et al., 2007). For this reason, PET has been approved for FTD diagnosis by the US Centers for Medicare and Medicaid Services. Amyloid imaging is generally negative in the FTDs (Rowe et al., 2007). Tau imaging should be very helpful, but is only starting to be applied to FTD. Clinically, anatomically, neuropathologically, and genetically, FTD comprises a heterogeneous set of disorders (Rascovsky et al., 2011) (Table 26.1). The clinical presentation depends on the region of the brain earliest and most affected by the disease (Sapolsky et al., 2010; Kirshner, 2012; Zhang et al., 2013; Agosta et al., 2015). It can present with a frontal-lobe syndrome, characterized by impulsivity and disinhibition, the so-called behavioral variant of FTD (bvFTD or classic Pick’s disease, affecting the frontotemporal poles; Fig. 26.28) (Liscic et al., 2007; Whitwell et al., 2009), with an aphasic syndrome, named PPA (with left hemispheric involvement) (Gorno-Tempini et al., 2011), or with progressive prosopagnosia, when the anterior portion of the right temporal lobe is affected (Josephs et al., 2009). PPA can be either semantic (svPPA, involving predominantly the left temporal tip; Fig. 26.29) or nonfluent (nfvPPA, involving the left anterior perisylvian area; Fig. 26.5). There is a third PPA variant, termed logopenic aphasia (lvPPA, involving the left posterior perisylvian area; Fig. 26.22) (Gorno-Tempini et al., 2004) which is most often associated with AD, rather than FTD, pathology (Mesulam et al., 2014). FTD can also co-occur with motor neurone disease, and atypical parkinsonian disorders,

Fig. 26.24. Imaging in Alzheimer’s disease therapy. Magnetic resonance imaging and 11C Pittsburgh compound B (PIB) positron emission tomography scans of an apolipoprotein E E4 heterozygote given bapineuzumab (2.0 mg/kg). The times indicated in the images represent time from bapineuzumab administration. (A) Baseline fluid-attenuated inversion recovery (FLAIR) image without evidence of ARIA-E. FLAIR sequence obtained at week 6 (C) shows bifrontal parenchymal hyperintensity (arrows: ARIA-E), which resolved by week 19 (D). Additionally, week 19 gradient-echo T2*-weighted sequence (F) shows the development of bifrontal microhemorrhages (ARIA-H: arrows) not present on previous images (not shown). A corresponding week 19 11C PIB scan (E) shows reduced 11C PIB uptake (arrows) compared with that at baseline in regions with ARIA-E and ARIA-H (arrows: B). ARIA-E, amyloid-related imaging abnormalities thought to be parenchymal vasogenic edema and sulcal effusions; ARIA-H, amyloid-related imaging abnormalities thought to be a result of microhemorrhages and hemosiderosis. (Reproduced from Sperling et al., 2012.)

Fig. 26.25. Metabolism in diffuse Lewy-body disease (DLB). 18F-2-deoxy-2-fluoro-D-glucose positron emission tomography from a 75-year-old man with DLB showing decreased metabolism in the lateral aspect of the occipital lobes (arrowheads) while having greater metabolism than the patient with Alzheimer’s disease (AD) in the posterior cingulate region (arrows).

548

J.C. MASDEU AND B. PASCUAL

Fig. 26.26. “Island sign” in diffuse Lewy-body disease (DLB). On magnetic resonance imaging, templates of the medial aspect of the brain, areas of decreased metabolism (18F-2-deoxy-2-fluoro-D-glucose positron emission tomography) in Alzheimer’s disease (AD) and decreased perfusion (H215O-PET) in DLB. Metabolism and perfusion are coupled in AD and DLB. Note involvement of the posterior cingulate gyrus in AD, but sparing of this region (arrow) in DLB. (Modified from Goker-Alpan et al., 2012.)

Fig. 26.27. Decreased presynaptic dopamine in diffuse Lewy-body disease (DLB). On an axial magnetic resonance imaging template, areas of decreased presynaptic dopamine (18F-FDOPA-PET) in a sample of patients with DLB. (Reproduced from Goker-Alpan et al., 2012.)

such as corticobasal degeneration and progressive supranuclear palsy. These two disorders are associated to tau pathology, and their clinical and pathologic features overlap: the clinical syndrome of progressive supranuclear palsy can be associated with corticobasal degeneration pathology, and vice versa (Josephs et al., 2011; Whitwell et al., 2013). The clinical syndromes correspond to well-defined neuroimaging. At stages beyond the initial gait impairment, progressive supranuclear palsy is relatively easy to diagnose clinically by the characteristic parkinsonism associated with markedly impaired postural reflexes and downward-gaze palsy; frank dementia, of a frontal-lobe type, only supervenes as

the disease advances. MRI shows minimal frontal atrophy (Agosta et al., 2015) but remarkable midbrain atrophy (hummingbird sign; Fig. 26.30), such that a decreased midbrain to pons area ratio on sagittal images distinguishes well this disorder (Massey et al., 2013). Corticobasal degeneration is characterized by progressive apraxia, accompanied by apractic agraphia when the left hemisphere is affected. Typically, both atrophy and decreased metabolism affect the superior parietal lobule (Fig. 26.31). This is the area of representation of the hand: thus corticobasal degeneration causes apraxia, while PPA, involving the perisylvian cortex, which subserves the mouth region of the motor strip (Fig. 26.5), is associated with aphasia. FTD pathology is heterogeneous and based on the type of neuronal lesions and protein inclusions: 40% or more of patients have tau pathology, about 50% have TAR DNA-binding protein 43 (TDP-43) pathology, and the remaining 10% have inclusions positive for fusedin sarcoma (FUS) or ubiquitin/p62 (Ferrari et al., 2014). There is a loose correspondence between the underlying pathology and the cortical location of the damage, which determines the clinical and imaging findings (Josephs et al., 2011) (Fig. 26.32). While bvFTD is associated with tau and TDP, as well as, rarely, FUS, semantic dementia and any motor syndrome are more often associated with TDP. All the other types of FTD are associated more often, but not always, with tau (Fig. 26.32). The advent of tau imaging should greatly help characterize the neuropathology in a given case, although it is to be determined whether 18F-AV-1451 and similar compounds bind to all types of tau tangles or miss some of them, such as the globose tangles of progressive supranuclear palsy. The importance of developing a TDP-43 PET ligand becomes clear from these data and from the recent studies emphasizing the correlation of TDP-43 pathology with cognitive impairment in AD (Josephs et al., 2014b). Mutations in three main genes are commonly associated with FTD: the microtubule-associated protein tau

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

549

Fig. 26.28. Behavioral form of frontotemporal dementia. Shown are 18F-2-deoxy-2-fluoro-D-glucose positron emission tomography (FDG)-positron emission tomography (PET) (A, B) and fluid-attenuated inversion recovery magnetic resonance imaging (MRI) (C) studies from a 51-year-old man with progressive speech apraxia and impaired planning, to the point of mutism and complete dependency for activities of daily living when studies B and C were obtained, on the same day. Metabolism was already decreased on the initial PET study, particularly on the frontal opercula and temporal tips, but it is much more obvious on the followup study, showing extensive frontotemporal hypometabolism. Note that the frontotemporal abnormality is much more obvious on the PET study (A, B) than on the MRI study (C), which shows frontal atrophy. (Reproduced from Masdeu, 2008.)

550

J.C. MASDEU AND B. PASCUAL

Fig. 26.29. Semantic dementia. Metabolism (18F-2-deoxy-2-fluoro-D-glucose positron emission tomography: FDG) and abeta (11C-PIB) positron emission tomography data superimposed to the magnetic resonance imaging (MRI) of a 65-year-old man with marked anomia, but preserved repetition. Note the marked left anterior temporal atrophy. Metabolism is markedly decreased on the left, but also, to a lesser degree, on the right temporal tip.

Fig. 26.30. Midbrain atrophy in progressive supranuclear palsy (PSP). Sagittal T1-weighted magnetic resonance imaging from a 79-year-old woman with PSP shows marked atrophy of the midbrain, which has the appearance of a hummingbird (hummingbird sign). Compare with the morphology of the midbrain at the same level in a healthy individual of a similar age.

(MAPT), granulin (GRN), and C9orf72 (Ferrari et al., 2014). Mutations in the charged multivesicular body protein 2B (CHMP2B), the valosin-containing protein (VCP), and ubiquilin 2 (UBQLN2) genes are rare causes of FTD. However, less than 20% of patients have identified mutations; this proportion may change as deeper genetic studies are conducted. Predisposing to FTD are variants of some genes, such as TMEM106B, RAB38/ CTSC, and a gene at the human leukocyte antigen locus, implicating the immune system (Ferrari et al., 2014). Unfortunately, there is little correspondence between the cortical location of the damage –and thus the clinical and imaging findings – and the type of mutation, suggesting that the damage is mediated by still poorly understood neurobiologic mechanisms. However, MAPT mutations have been associated with greater anteromedial temporal atrophy, and GRN mutations with greater parietal atrophy, while C9ORF72 mutations were associated with symmetric atrophy predominantly involving dorsolateral,

medial, and orbitofrontal lobes, with additional loss in anterior temporal lobes, parietal lobes, occipital lobes, and cerebellum (Whitwell et al., 2012b) (Fig. 26.33). In a longitudinal follow-up, whole-brain atrophy progressed faster with GRN mutations than with those in C9ORF72 or MAPT. C9ORF72 mutations showed greater rates of atrophy in the left cerebellum and right occipital lobe than MAPT (Whitwell et al., 2015a).

Altered connectivity As in AD, connectivity in FTD has been studied with BOLD fMRI and with techniques that use diffusion imaging on MR, such as DTI and kurtosis.

FUNCTIONAL CONNECTIVITY (BOLD FMRI) Pathologic and compensatory mechanisms in FTD result in abnormal functional connectivity across networks. Specific network changes depend on the cortical regions most

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA

551

Fig. 26.31. Metabolism in corticobasal degeneration. Axial sections of an 18F-2-deoxy-2-fluoro-D-glucose (FDG) positron emission tomography (PET) study from a 47-year-old man with progressive agraphia and apraxia, as well as right-sided parkinsonism. Metabolism in the association cortex of the frontal and parietal lobe is decreased (white arrows), as well as in the ipsilateral thalamus (arrowhead) and lenticular nucleus (red arrow). Note that the greatest decrease in metabolism is in the higher sections, corresponding to the area of representation of the hand in the motor strip. (Reproduced from Masdeu, 2008.)

Fig. 26.32. Misfolded tau or TAR DNA-binding protein 43 (TDP-43) in the frontotemporal dementias. Percentages of cases with each of the clinical syndromes that have either tau or TDP-43 as the predominant brain pathology. PSP, progressive supranuclear palsy; CBD, corticobasal degeneration; nfvPPA, nonfluent primary progressive aphasia; bvFTD, behavioral variant of frontotemporal dementia; svPPA, semantic variant of primary progressive aphasia; FTD-MND, frontotemporal dementia– motor neuron disease. (Data from Josephs et al., 2011.)

affected by a given variety of FTD. Thus, bvFTD affects the emotional salience network, while svPPA affects the anterior portion of the semantic network, corresponding to a temporal pole–subgenual cingulate–ventral striatum– amygdala network (Guo et al., 2013). nfvPPA affects the

frontal operculum, primary and supplementary motor cortices, and inferior parietal lobule bilaterally, linking the language and motor systems that enable speech fluency (Seeley et al., 2009). Asymmetric degeneration of this system may reflect its accentuated functional and connectional asymmetry in healthy humans. lvPPA shows reduced connectivity in left temporal language network and inferior parietal and prefrontal regions of the left working-memory network compared with controls and typical AD (Whitwell et al., 2015c). Both groups show reduced connectivity in the parietal regions of the right working-memory network compared with controls. Only typical AD shows reduced ventral default-mode network connectivity compared with controls (Whitwell et al., 2015c).

DIFFUSION TENSOR IMAGING OR KURTOSIS In FTD, decreased fractional anisotropy or increased radial diffusivity is identified in tracts that interconnect the gray-matter regions that are atrophic, supporting the hypothesis that varieties of FTD involve different and specific brain networks (Whitwell et al., 2010). In FTD, white-matter changes involve the superior longitudinal fasciculus, uncinate fasciculus, cingulum bundle, and corpus callosum (Mahoney et al., 2014). When compared to AD, FTD is associated with greater fractional anisotropy reduction in frontal brain regions, as well as in the

552

J.C. MASDEU AND B. PASCUAL

Fig. 26.33. Brain atrophy patterns associated with mutations in MAPT, GRN, and C9ORF72. In yellow-red and projected on rendered images, areas of the brain with significant atrophy in groups of patients with mutations in each of the three genes. (Reproduced from Whitwell et al., 2012b.) With permission from Oxford University Press.

anterior corpus callosum (Zhang et al., 2009; McMillan et al., 2012). In bvFTD, abnormal white-matter diffusivity is observed in frontal white matter (Whitwell et al., 2010; Agosta et al., 2012; Maruyama et al., 2013), and the orbitofrontal and anterior temporal tracts (Lam et al., 2014), as well as the anterior cingulum bundle (Santillo et al., 2013). Main language tracts are affected in the three variants of PPA. In nfvPPA, white-matter changes are observed in the pathways connecting the speech production network, with tract abnormalities observed in the superior longitudinal fasciculus, and reductions of fractional anisotropy and increased mean diffusivity in the left inferior frontal lobe, insula, supplementary motor area, and striatal regions (Whitwell et al., 2010; Galantucci et al., 2011; Zhang et al., 2013; Mandelli et al., 2014). The frontal aslant tract, connecting Broca’s region with the anterior cingulate and presupplementary motor area, is affected in nfvPPA but not in svPPA, and seems to be involved in verbal fluency, more than in grammatic or repetition processes (Catani et al., 2013). In svPPA, the main changes are identified in the pathways connecting the semantic processing network, with tract abnormalities observed in the inferior longitudinal fasciculus and uncinate fasciculus, predominantly in the left anterior temporal lobe (Whitwell et al., 2010; Agosta

et al., 2012; Galantucci et al., 2011; Catani et al., 2013; Zhang et al., 2013; Mandelli et al., 2014). Language tracts are more preserved in patients with lvPPA and the principal white-matter changes are observed in the temporoparietal component of the cingulum bundle (Galantucci et al., 2011). Progression of white-matter diffusivity changes over time may be more pronounced and specific than progression of gray-matter changes in FTD (Sajjadi et al., 2013; Lam et al., 2014). In one study (Lam et al., 2014) mean diffusivity was most sensitive in detecting baseline changes while fractional anisotropy and radial diffusivity revealed greatest changes over time. Disease progression involved posterior temporal and occipital white matter in bvFTD, right frontotemporal white matter in nfvPPA, and bilateral frontotemporal tracts in svPPA (Lam et al., 2014). In another study with 1-year followup, all patients with nfvPPA evolved to either corticobasal degeneration or progressive supranuclear palsy, and showed white-matter abnormalities involving the entire cerebrum, suggesting a diffuse pathologic process in the white matter of these tauopathies and not merely a function of disease severity, since gray-matter analysis consisting of group-level voxel-based morphometry revealed only focal areas of atrophy (Sajjadi et al., 2013). Patients with AD and svPPA did not show this degree of white-matter changes.

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA According to analyses based on small numbers of genetic cases of bvFTD, MAPT mutation shows consistent alterations in left uncinate fasciculus across diffusivity metrics (Lam et al., 2014; Mahoney et al., 2014, 2015). Alterations detected in the C9ORF72 mutation are less extensive and involve the corpus callosum and cingulum bundle. On direct comparison of MAPT with C9ORF72, those with MAPT showed alterations in white matter within the left anterior temporal pole, as measured by reduced fractional anisotropy. However, there are not differences between patients with MAPT and C9ORF72 mutations and those with sporadic bvFTD (Mahoney et al., 2014, 2015).

Metabolism Memory test performance may not distinguish between AD and FTD dementia syndromes (Frisch et al., 2013). In clinical practice, this may lead to misdiagnosis of FTD patients with poor memory performance. However, whereas in AD patients memory test performance correlates with 18F-FDG PET changes in precuneus, performance in FTD patients correlates with changes in frontal cortex, with little overlap between the two disorders (Piolino et al., 2007; Frisch et al., 2013). In FTD, many studies have found decreased metabolic activity in frontal and temporal regions of the cortex, as well as some subcortical regions, such as the caudate nucleus or thalamus (Salmon et al., 2003; Diehl et al., 2004; Jeong et al., 2005; Foster et al., 2007; Kanda et al., 2008; Mosconi et al., 2008; Teune et al., 2010) (Figs 26.5, 26.28, 26.29, and 26.31). As the disease worsens, from the frontal and anterotemporal regions the metabolic changes spread into the parietal and posterior temporal cortices (Diehl-Schmid et al., 2007). In the clinic, 18F-FDG PET in FTD is commonly reserved for patients with suspected FTD without characteristic structural neuroimaging results. Nearly half of the patients with early bvFTD, but a normal MRI, have an abnormal 18F-FDG PET, thus helping to exclude psychiatric and other neurodegenerative disorders (Kerklaan et al., 2014). Language phenotype in PPA is closely related to metabolic changes that are focal and anatomically distinct among subtypes (Rabinovici et al., 2008). Most patients with nfvPPA show asymmetric left frontal and insular hypometabolism (Fig. 26.5), sdPPA patients show prominent bilateral anterior temporal hypometabolism, left greater than right (Fig. 26.29), and finally, patients with lvPPA show the greatest metabolic decrements in the left parietal and posterolateral temporal lobes, but also in the left frontal lobe (Fig. 26.22). In lvPPE, there is greater left laterotemporal hypometabolism, and less right temporomedial and posterior cingulate hypometabolism than in overall

553

AD (Madhavan et al., 2013). Focal decreased metabolism correlates well with focal atrophy and their severity correlates well with the severity of language impairment (Gorno-Tempini et al., 2011). To date, mutations associated with FTD have failed to show a pattern of metabolism helpful to discriminate different genotypes, possibly because the sample sizes were small (Jacova et al., 2013; Josephs et al., 2014a). However, 18 F-FDG PET may be useful to detect brain changes in subjects carrying a FTD-obligatory mutation (Deters et al., 2014).

Amyloid deposition Because pathologic abeta deposition is a key component of AD but not a feature of FTD, amyloid PET is a valuable clinical tool to differentiate AD from FTD, especially in young patients in whom age-related amyloid deposition is less common. Studies on amyloid PET have shown very low rates of 11C-PIB, 18Fflorbetapir, or 18F-florbetaben positivity in most FTD patients, providing good discrimination from AD (Rabinovici et al., 2007, 2011; Villemagne et al., 2011a; Kobylecki et al., 2015). However, visual rating of FTD scans is challenging, with a higher rate of discordance between raters than when they have to separate AD from control subjects (Kobylecki et al., 2015). For this reason, software packages are being built to facilitate the comparison of individual scans with reference data, as has been done with 18F-FDG PET (Thiele et al., 2013; Herholz, 2014). In patients with PPA, the level of abeta burden varies considerably across different variants (Rabinovici et al., 2008; Leyton et al., 2011; Ikeda et al., 2014). Abnormal 11 C-PIB retention can be identified in most patients with lvPPA (Rabinovici et al., 2008; Leyton et al., 2011; Whitwell et al., 2015b), confirming that this variant represents a common presentation of AD. Abeta distribution across cortical regions is identical in lvPPA and in typical AD, although the total load is lower in the aphasic cases (Leyton et al., 2011). Among the small proportion of patients with lvPPA who do not have abeta deposition, up to 50% may have GRN mutations (Josephs et al., 2014a). Increased 11C-PIB is uncommon in patients with the nonfluent or semantic variants, confirming that these PPA variants are rarely associated with AD pathology (Drzezga et al., 2008; Mormino et al., 2009; Leyton et al., 2011). Occasional 11C-PIB-positive scans of patients with nfvPPA and svPPA present amyloid deposition patterns similar to AD, but with lesser amounts of amyloid (Rabinovici et al., 2008; Leyton et al., 2011). However, these patients had metabolism studies characteristic of their type of FTD, not AD. It is possible that the amyloid deposition was an

554

J.C. MASDEU AND B. PASCUAL

age-related phenomenon, not linked to the disease (Rabinovici et al., 2008, 2011).

Inflammation Inflammation has seldom been imaged in FTD (Fig. 26.22), but both genetic studies (Ferrari et al., 2014) and studies looking at associations with autoimmune disease (Miller et al., 2013) suggest that immune mechanisms may be important in the sporadic form of FTD, particularly in TDP-43 FTD. In a small pilot study (Cagnin et al., 2004), 11C-PK11195 PET showed increased binding in the typically affected frontotemporal brain regions.

NETWORK ABNORMALITIES IN FTD AND AD It is likely that a better understanding of their neurobiology will change the nosologic understanding of AD and FTD. Using MRI and PET, a key step in this direction has been taken by disclosing network properties in these disorders. AD and other neurodegenerative dementias are characterized by cortex thinning, more profound and extensive as the disease worsens (Dickerson et al., 2009). This thinning is not uniform across the brain, but involves areas that are highly specific for each disorder, so much so that “cortical signatures” have been described for AD and each of the FTD disorders (Fig. 26.34)

Fig. 26.34. Natural brain networks affected in each neurodegenerative phenotype. Seeds in areas of early and maximal atrophy in each dementia phenotype (top row) have predominant functional connectivity on blood oxygen level-dependent magnetic resonance imaging to distinct and characteristic brain networks, which become progressively affected as the disease worsens. AD, Alzheimer’s disease; bvFTD, behavioral variant of frontotemporal dementia; svPPA, semantic variant of primary progressive aphasia; nfvPPA, nonfluent primary progressive aphasia; CBD, corticobasal degeneration; R Ang, right angular gyrus; R FI, right inferior frontal gyrus; L TPole, left temporal pole; L IFG, left inferior frontal gyrus; R PMC, right premotor cortex; fMRI, functional magnetic resonance imaging. (Reproduced from Seeley et al., 2009.)

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA (Dickerson et al., 2009; Seeley et al., 2009). Remarkably, when the area first developing thinning in each dementing disorder is used as a seed to study with fMRI its physiologic resting functional connectivity with the rest of the brain, it turns out that it connects most strongly with those areas which will be subsequently affected in the same disorder (Fig. 26.34) (Seeley et al., 2009; Zhou et al., 2012; Lehmann et al., 2013b). More recently, similar network relationships have been shown with amyloid imaging in AD (Sepulcre and Masdeu, 2015). Thus, the Hebbian principle, “neurons that fire together, wire together” (Hebb, 1961) has been extended to say: “neurons that wire together, die together” (Sepulcre et al., 2012). This finding, coupled with animal data showing that abnormal tau (de Calignon et al., 2012; Liu et al., 2012) or a-synuclein (Luk et al., 2012) can propagate transsynaptically from neuron to neuron in a prion-like fashion, has strengthened the hypothesis that neurodegenerative dementias are disorders of misfolded proteins propagating across brain networks and causing neuronal death (Prusiner, 2013). In this scenario, inflammatory cells could be targeting neurons headed for apoptosis, or apoptotic byproducts, but they could also be pathogenetic (Fig. 26.2). For instance, through a prion-like mechanism misfolded proteins could change the antigenic properties of healthy neurons, making them the target of autoimmune attack (Franklin et al., 2014). Imaging tau and inflammation using PET compounds in patients being treated with immune therapy could help clarify these processes.

CONCLUSION In the past few years, neuroimaging has provided powerful data on the neurobiologic changes associated with the disorders causing dementia. Imaging is emerging as a powerful biomarker to define target engagement in therapeutic trials. However, much work remains. Only recently, the correlation of imaging changes with gene variants or mutations predisposing to the various disorders has been undertaken on a large scale. Epistatic effects should now be evaluated more extensively, and work should continue on epigenetic factors influencing the development of abeta deposition and markers of neurodegeneration. Studies with PET markers of inflammation, combined with abeta, tau, and morphometric imaging, should provide data on the likelihood that inflammation in AD and other disorders is reactive or causal. The effect of therapies aimed at reducing abeta deposition or tau spread in the preclinical stages of the disease, when cognition is normal, needs to be monitored with neuroimaging.

555

FUNDING This work was supported by the Nantz National Alzheimer Center, Houston Methodist Neurological Institute, and the Houston Methodist Research Institute.

ACKNOWLEDGMENTS AND POTENTIAL CONFLICTS OF INTEREST Dr. Masdeu is a consultant for the General Electric Company. During the writing of this chapter he was the Editor-in-Chief of the Journal of Neuroimaging.

ABBREVIATIONS 18

F-FDG 18F-2-deoxy-2-fluoro-D-glucose; AD Alzheimer’s disease; APOE apoliprotein E; ASL arterial spin labeling; BOLD blood oxygenation level-dependent; bvFTD behavioral variant of frontotemporal dementia; CADASIL cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CBD corticobasal degeneration; CSF cerebrospinal fluid; CT computed tomography; CTE chronic traumatic encephalopathy; DLB diffuse Lewy-body dementia; DTI diffusion tensor imaging; DWI diffusion-weighted imaging; FLAIR fluid-attenuated inversion recovery; fMRI functional magnetic resonance imaging; FTD frontotemporal dementia; GRN progranulin gene; lvPPA logopenic aphasia; MCI mild cognitive impairment; MND-FTD frontotemporal dementia with motor neuron disease findings; MRI magnetic resonance imaging; nfvPPA nonfluent primary progressive aphasia; PET positron emission tomography; PPA primary progressive aphasia; PSP progressive supranuclear palsy; ROC receiver operating characteristic; SPECT single-photon emission computed tomography; svFTD semantic variant of frontotemporal dementia; TBI traumatic brain injury; TSPO translocator protein

REFERENCES Agosta F, Pievani M, Sala S et al. (2011). White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology 258: 853–863. Agosta F, Scola E, Canu E et al. (2012). White matter damage in frontotemporal lobar degeneration spectrum. Cereb Cortex 22: 2705–2714. Agosta F, Galantucci S, Magnani G et al. (2015). MRI signatures of the frontotemporal lobar degeneration continuum. Hum Brain Mapp 36: 2602–2614. Albert MS, DeKosky ST, Dickson D et al. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on AgingAlzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7: 270–279. American Psychiatric Association (1994). Diagnostic and statistical manual of mental disorders: DSM-IV. American Psychiatric Association, Washington, DC.

556

J.C. MASDEU AND B. PASCUAL

American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders: DSM-5. American Psychiatric Association, Washington, DC. Apostolova LG, Hwang KS, Medina LD et al. (2011). Cortical and hippocampal atrophy in patients with autosomal dominant familial Alzheimer’s disease. Dement Geriatr Cogn Disord 32: 118–125. Bacskai BJ, Frosch MP, Freeman SH et al. (2007). Molecular imaging with Pittsburgh Compound B confirmed at autopsy: a case report. Arch Neurol 64: 431–434. Bakkour A, Morris JC, Wolk DA et al. (2013). The effects of aging and Alzheimer’s disease on cerebral cortical anatomy: specificity and differential relationships with cognition. Neuroimage 76: 332–344. Ballard CG, Burton EJ, Barber R et al. (2004). NINDS AIREN neuroimaging criteria do not distinguish stroke patients with and without dementia. Neurology 63: 983–988. Barnes DE, Kaup A, Kirby KA et al. (2014). Traumatic brain injury and risk of dementia in older veterans. Neurology 83: 312–319. Barrio JR, Small GW, Wong KP et al. (2015). In vivo characterization of chronic traumatic encephalopathy using [F-18]FDDNP PET brain imaging. Proc Natl Acad Sci U S A 112: E2039–E2047. Bateman RJ, Xiong C, Benzinger TL et al. (2012). Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 367: 795–804. Becker JA, Hedden T, Carmasin J et al. (2011). Amyloid-beta associated cortical thinning in clinically normal elderly. Ann Neurol 69: 1032–1042. Boles Ponto LL, Magnotta VA, Moser DJ et al. (2006). Global cerebral blood flow in relation to cognitive performance and reserve in subjects with mild memory deficits. Mol Imaging Biol 8: 363–372. Bondi MW, Houston WS, Eyler LT et al. (2005). fMRI evidence of compensatory mechanisms in older adults at genetic risk for Alzheimer disease. Neurology 64: 501–508. Bookheimer SY, Strojwas MH, Cohen MS et al. (2000). Patterns of brain activation in people at risk for Alzheimer’s disease. N Engl J Med 343: 450–456. Bozoki AC, Korolev IO, Davis NC et al. (2012). Disruption of limbic white matter pathways in mild cognitive impairment and Alzheimer’s disease: a DTI/FDG-PET study. Hum Brain Mapp 33: 1792–1802. Braak H, Thal DR, Ghebremedhin E et al. (2011). Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J Neuropathol Exp Neurol 70: 960–969. Braskie MN, Medina LD, Rodriguez-Agudelo Y et al. (2012). Increased fMRI signal with age in familial Alzheimer’s disease mutation carriers. Neurobiol Aging 33 (424): e411–e421. Brun A, Englund E (1981). Regional pattern of degeneration in Alzheimer’s disease: neuronal loss and histopathological grading. Histopathology 5: 549–564. Burggren AC, Zeineh MM, Ekstrom AD et al. (2008). Reduced cortical thickness in hippocampal subregions among cognitively normal apolipoprotein E e4 carriers. Neuroimage 41: 1177–1183. Burton EJ, Barber R, Mukaetova-Ladinska EB et al. (2009). Medial temporal lobe atrophy on MRI differentiates

Alzheimer’s disease from dementia with Lewy bodies and vascular cognitive impairment: a prospective study with pathological verification of diagnosis. Brain 132: 195–203. Cagnin A, Brooks DJ, Kennedy AM et al. (2001). In-vivo measurement of activated microglia in dementia. Lancet 358: 461–467. Cagnin A, Rossor M, Sampson EL et al. (2004). In vivo detection of microglial activation in frontotemporal dementia. Ann Neurol 56: 894–897. Caroli A, Prestia A, Chen K et al. (2012). Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison. J Nucl Med 53: 592–600. Carter SF, Scholl M, Almkvist O et al. (2012). Evidence for astrocytosis in prodromal Alzheimer disease provided by 11 C-deuterium-L-deprenyl: a multitracer PET paradigm combining 11C-Pittsburgh compound B and 18F-FDG. J Nucl Med 53: 37–46. Castellano JM, Kim J, Stewart FR et al. (2011). Human apoE isoforms differentially regulate brain amyloid-beta peptide clearance. Sci Transl Med 3. 89ra57. Catani M, Mesulam MM, Jakobsen E et al. (2013). A novel frontal pathway underlies verbal fluency in primary progressive aphasia. Brain 136: 2619–2628. Chauveau F, Boutin H, Van Camp N et al. (2008). Nuclear imaging of neuroinflammation: a comprehensive review of [11C]PK11195 challengers. Eur J Nucl Med Mol Imaging 35: 2304–2319. Chen K, Ayutyanont N, Langbaum JB et al. (2011a). Characterizing Alzheimer’s disease using a hypometabolic convergence index. Neuroimage 56: 52–60. Chen Y, Wolk DA, Reddin JS et al. (2011b). Voxel-level comparison of arterial spin-labeled perfusion MRI and FDGPET in Alzheimer disease. Neurology 77: 1977–1985. Chetelat G, Desgranges B, Landeau B et al. (2008a). Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer’s disease. Brain 131: 60–71. Chetelat G, Fouquet M, Kalpouzos G et al. (2008b). Threedimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry. Neuropsychologia 46: 1721–1731. Chetelat G, Villemagne VL, Bourgeat P et al. (2010). Relationship between atrophy and beta-amyloid deposition in Alzheimer disease. Ann Neurol 67: 317–324. Chetelat G, Villemagne VL, Pike KE et al. (2012a). Relationship between memory performance and betaamyloid deposition at different stages of Alzheimer’s disease. Neurodegener Dis 10: 141–144. Chetelat G, Villemagne VL, Villain N et al. (2012b). Accelerated cortical atrophy in cognitively normal elderly with high beta-amyloid deposition. Neurology 78: 477–484. Chiang GC, Insel PS, Tosun D et al. (2011). Identifying cognitively healthy elderly individuals with subsequent memory decline by using automated MR temporoparietal volumes. Radiology 259: 844–851.

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA Chien DT, Bahri S, Szardenings AK et al. (2013). Early clinical PET imaging results with the novel PHF-tau radioligand [F-18]-T807. J Alzheimers Dis 34: 457–468. Chien DT, Szardenings AK, Bahri S et al. (2014). Early clinical PET imaging results with the novel PHF-tau radioligand [F18]-T808. J Alzheimers Dis 38: 171–184. Choi SR, Schneider JA, Bennett DA et al. (2012). Correlation of amyloid PET ligand florbetapir F 18 binding with abeta aggregation and neuritic plaque deposition in postmortem brain tissue. Alzheimer Dis Assoc Disord 26: 8–16. Chui HC, Ramirez-Gomez L (2015). Clinical and imaging features of mixed Alzheimer and vascular pathologies. Alzheimers Res Ther 7: 21. Clerx L, Visser PJ, Verhey F et al. (2012). New MRI markers for Alzheimer’s disease: a meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements. J Alzheimers Dis 29: 405–429. Crivello F, Lemaitre H, Dufouil C et al. (2010). Effects of ApoE-epsilon4 allele load and age on the rates of grey matter and hippocampal volumes loss in a longitudinal cohort of 1186 healthy elderly persons. Neuroimage 53: 1064–1069. de Calignon A, Polydoro M, Suarez-Calvet M et al. (2012). Propagation of tau pathology in a model of early Alzheimer’s disease. Neuron 73: 685–697. de la Torre JC (2002). Alzheimer disease as a vascular disorder: nosological evidence. Stroke 33: 1152–1162. de Leeuw FE, de Groot JC, Achten E et al. (2001). Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 70: 9–14. de Souza LC, Chupin M, Bertoux M et al. (2013). Is hippocampal volume a good marker to differentiate Alzheimer’s disease from frontotemporal dementia? J Alzheimers Dis 36: 57–66. Dennis NA, Browndyke JN, Stokes J et al. (2010). Temporal lobe functional activity and connectivity in young adult APOE varepsilon4 carriers. Alzheimers Dement 6: 303–311. Deters KD, Risacher SL, Farlow MR et al. (2014). Cerebral hypometabolism and grey matter density in MAPT intron 10 + 3 mutation carriers. Am J Neurodegener Dis 3: 103–114. Devanand DP, Mikhno A, Pelton GH et al. (2010a). Pittsburgh compound B ((11)C-PIB) and fluorodeoxyglucose ((18)FFDG) PET in patients with Alzheimer disease, mild cognitive impairment, and healthy controls. J Geriatr Psychiatry Neurol 23: 185–198. Devanand DP, Van Heertum RL, Kegeles LS et al. (2010b). (99m)Tc hexamethyl-propylene-aminoxime single-photon emission computed tomography prediction of conversion from mild cognitive impairment to Alzheimer disease. Am J Geriatr Psychiatry 18: 959–972. Dickerson BC, Wolk DA (2012). MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults. Neurology 78: 84–90. Dickerson BC, Salat DH, Bates JF et al. (2004). Medial temporal lobe function and structure in mild cognitive impairment. Ann Neurol 56: 27–35.

557

Dickerson BC, Bakkour A, Salat DH et al. (2009). The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb Cortex 19: 497–510. Dickerson BC, Stoub TR, Shah RC et al. (2011). Alzheimersignature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 76: 1395–1402. Diehl J, Grimmer T, Drzezga A et al. (2004). Cerebral metabolic patterns at early stages of frontotemporal dementia and semantic dementia. A PET study. Neurobiol Aging 25: 1051–1056. Diehl-Schmid J, Grimmer T, Drzezga A et al. (2007). Decline of cerebral glucose metabolism in frontotemporal dementia: a longitudinal 18F-FDG-PET-study. Neurobiol Aging 28: 42–50. Donix M, Burggren AC, Suthana NA et al. (2010). Longitudinal changes in medial temporal cortical thickness in normal subjects with the APOE-4 polymorphism. Neuroimage 53: 37–43. Douaud G, Jbabdi S, Behrens TE et al. (2011). DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer’s disease. Neuroimage 55: 880–890. Drzezga A, Grimmer T, Henriksen G et al. (2008). Imaging of amyloid plaques and cerebral glucose metabolism in semantic dementia and Alzheimer’s disease. Neuroimage 39: 619–633. Drzezga A, Becker JA, Van Dijk KR et al. (2011). Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain 134: 1635–1646. Edison P, Archer HA, Gerhard A et al. (2008). Microglia, amyloid, and cognition in Alzheimer’s disease: an 11C (R) PK11195-PET and 11C PIB-PET study. Neurobiol Dis 32: 412–419. Ercoli LM, Small GW, Siddarth P et al. (2012). Assessment of dementia risk in aging adults using both FDG-PET and FDDNP-PET imaging. Int J Geriatr Psychiatry 27: 1017–1027. Esiri MM, Wilcock GK, Morris JH (1997). Neuropathological assessment of the lesions of significance in vascular dementia. J Neurol Neurosurg Psychiatry 63: 749–753. Fellgiebel A, Wille P, Muller MJ et al. (2004). Ultrastructural hippocampal and white matter alterations in mild cognitive impairment: a diffusion tensor imaging study. Dement Geriatr Cogn Disord 18: 101–108. Ferrari R, Hernandez DG, Nalls MA et al. (2014). Frontotemporal dementia and its subtypes: a genome-wide association study. Lancet Neurol 13: 686–699. Ferrer I, Boada Rovira M, Sanchez Guerra ML et al. (2004). Neuropathology and pathogenesis of encephalitis following amyloid-beta immunization in Alzheimer’s disease. Brain Pathol 14: 11–20. Ferretti MT, Cuello AC (2011). Does a pro-inflammatory process precede Alzheimer’s disease and mild cognitive impairment? Curr Alzheimer Res 8: 164–174. Filippi M, Agosta F (2011). Structural and functional network connectivity breakdown in Alzheimer’s disease studied

558

J.C. MASDEU AND B. PASCUAL

with magnetic resonance imaging techniques. J Alzheimers Dis 24: 455–474. Fleisher AS, Houston WS, Eyler LT et al. (2005). Identification of Alzheimer disease risk by functional magnetic resonance imaging. Arch Neurol 62: 1881–1888. Folkersma H, Boellaard R, Yaqub M et al. (2011). Widespread and prolonged increase in (R)-(11)C-PK11195 binding after traumatic brain injury. J Nucl Med 52: 1235–1239. Fortea J, Sala-Llonch R, Bartres-Faz D et al. (2010). Increased cortical thickness and caudate volume precede atrophy in PSEN1 mutation carriers. J Alzheimers Dis 22: 909–922. Fortea J, Vilaplana E, Alcolea D et al. (2014). Cerebrospinal fluid beta-amyloid and phospho-tau biomarker interactions affecting brain structure in preclinical Alzheimer disease. Ann Neurol 76: 223–230. Foster NL, Heidebrink JL, Clark CM et al. (2007). FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain 130: 2616–2635. Fox PT, Raichle ME, Mintun MA et al. (1988). Nonoxidative glucose consumption during focal physiologic neural activity. Science 241: 462–464. Franklin BS, Bossaller L, De Nardo D et al. (2014). The adaptor ASC has extracellular and ‘prionoid’ activities that propagate inflammation. Nat Immunol 15: 727–737. Frisch S, Dukart J, Vogt B et al. (2013). Dissociating memory networks in early Alzheimer’s disease and frontotemporal lobar degeneration – a combined study of hypometabolism and atrophy. PLoS One 8: e55251. Galantucci S, Tartaglia MC, Wilson SM et al. (2011). White matter damage in primary progressive aphasias: a diffusion tensor tractography study. Brain 134: 3011–3029. Galvin JE, Price JL, Yan Z et al. (2011). Resting bold fMRI differentiates dementia with Lewy bodies vs Alzheimer disease. Neurology 76: 1797–1803. Goker-Alpan O, Masdeu JC, Kohn PD et al. (2012). The neurobiology of glucocerebrosidase-associated parkinsonism: a positron emission tomography study of dopamine synthesis and regional cerebral blood flow. Brain 135: 2440–2448. Gorno-Tempini ML, Dronkers NF, Rankin KP et al. (2004). Cognition and anatomy in three variants of primary progressive aphasia. Ann Neurol 55: 335–346. Gorno-Tempini ML, Hillis AE, Weintraub S et al. (2011). Classification of primary progressive aphasia and its variants. Neurology 76: 1006–1014. Gour N, Ranjeva JP, Ceccaldi M et al. (2011). Basal functional connectivity within the anterior temporal network is associated with performance on declarative memory tasks. Neuroimage 58: 687–697. Graff-Radford J, Murray ME, Lowe VJ et al. (2014). Dementia with Lewy bodies: basis of cingulate island sign. Neurology 83: 801–809. Groom GN, Junck L, Foster NL et al. (1995). PET of peripheral benzodiazepine binding sites in the microgliosis of Alzheimer’s disease. J Nucl Med 36: 2207–2210. Guo CC, Gorno-Tempini ML, Gesierich B et al. (2013). Anterior temporal lobe degeneration produces widespread network-driven dysfunction. Brain 136: 2979–2991.

Gurol ME, Viswanathan A, Gidicsin C et al. (2013). Cerebral amyloid angiopathy burden associated with leukoaraiosis: a positron emission tomography/magnetic resonance imaging study. Ann Neurol 73: 529–536. Hahnel S, Stippich C, Weber I et al. (2008). Prevalence of cerebral microhemorrhages in amateur boxers as detected by 3T MR imaging. AJNR Am J Neuroradiol 29: 388–391. Head D, Bugg JM, Goate AM et al. (2012). Exercise engagement as a moderator of the effects of APOE genotype on amyloid deposition. Arch Neurol 69: 636–643. Hebb DO (1961). Distinctive features of learning in the higher animal. In: JF Delafresnaye (Ed.), Brain Mechanisms and Learning. Oxford University Press, London. Hedden T, Van Dijk KR, Becker JA et al. (2009). Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci 29: 12686–12694. Heister D, Brewer JB, Magda S et al. (2011). Predicting MCI outcome with clinically available MRI and CSF biomarkers. Neurology 77: 1619–1628. Herholz K (2014). Guidance for reading FDG PET scans in dementia patients. Q J Nucl Med Mol Imaging 58: 332–343. Hoozemans JJ, Rozemuller AJ, van Haastert ES et al. (2011). Neuroinflammation in Alzheimer’s disease wanes with age. J Neuroinflammation 8: 171. Hua X, Leow AD, Parikshak N et al. (2008). Tensorbased morphometry as a neuroimaging biomarker for Alzheimer’s disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage 43: 458–469. Ikeda M, Tashiro Y, Takai E et al. (2014). CSF levels of Abeta1-38/Abeta1-40/Abeta1-42 and (11)C PiB-PET studies in three clinical variants of primary progressive aphasia and Alzheimer’s disease. Amyloid 21: 238–245. Ikonomovic MD, Klunk WE, Abrahamson EE et al. (2008). Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer’s disease. Brain 131: 1630–1645. Jack Jr CR, Lowe VJ, Senjem ML et al. (2008). 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain 131: 665–680. Jack Jr CR, Knopman DS, Jagust WJ et al. (2010a). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9: 119–128. Jack CR, Wiste HJ, Vemuri P et al. (2010b). Brain betaamyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer’s disease. Brain 133: 3336–3348. Jack CR, Vemuri P, Wiste HJ et al. (2011). Evidence for ordering of Alzheimer disease biomarkers. Arch Neurol 68: 1526–1535. Jack Jr CR, Knopman DS, Weigand SD et al. (2012). An operational approach to National Institute on AgingAlzheimer’s Association criteria for preclinical Alzheimer disease. Ann Neurol 71: 765–775. Jack Jr CR, Knopman DS, Jagust WJ et al. (2013). Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 12: 207–216.

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA Jacobs HI, Van Boxtel MP, Heinecke A et al. (2012). Functional integration of parietal lobe activity in early Alzheimer disease. Neurology 78: 352–360. Jacova C, Hsiung GY, Tawankanjanachot I et al. (2013). Anterior brain glucose hypometabolism predates dementia in progranulin mutation carriers. Neurology 81: 1322–1331. Jagust W, Thisted R, Devous Sr MD et al. (2001). SPECT perfusion imaging in the diagnosis of Alzheimer’s disease: a clinical-pathologic study. Neurology 56: 950–956. Jagust W, Reed B, Mungas D et al. (2007). What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia? Neurology 69: 871–877. Jeong Y, Cho SS, Park JM et al. (2005). 18F-FDG PET findings in frontotemporal dementia: an SPM analysis of 29 patients. J Nucl Med 46: 233–239. Johnson KA, Jones K, Holman BL et al. (1998). Preclinical prediction of Alzheimer’s disease using SPECT. Neurology 50: 1563–1571. Johnson SC, Schmitz TW, Trivedi MA et al. (2006). The influence of Alzheimer disease family history and apolipoprotein E epsilon4 on mesial temporal lobe activation. J Neurosci 26: 6069–6076. Johnson KA, Gregas M, Becker JA et al. (2007). Imaging of amyloid burden and distribution in cerebral amyloid angiopathy. Ann Neurol 62: 229–234. Johnson KA, Minoshima S, Bohnen NI et al. (2013a). Appropriate use criteria for amyloid PET: a report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association. J Nucl Med 54: 476–490. Johnson KA, Sperling RA, Gidicsin CM et al. (2013b). Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer’s disease dementia, mild cognitive impairment, and normal aging. Alzheimers Dement 9: S72–S83. Josephs KA, Whitwell JL, Knopman DS et al. (2009). Two distinct subtypes of right temporal variant frontotemporal dementia. Neurology 73: 1443–1450. Josephs KA, Hodges JR, Snowden JS et al. (2011). Neuropathological background of phenotypical variability in frontotemporal dementia. Acta Neuropathol 122: 137–153. Josephs KA, Duffy JR, Strand EA et al. (2014a). Progranulinassociated PiB-negative logopenic primary progressive aphasia. J Neurol 261: 604–614. Josephs KA, Whitwell JL, Weigand SD et al. (2014b). TDP-43 is a key player in the clinical features associated with Alzheimer’s disease. Acta Neuropathol 127: 811–824. Kadir A, Marutle A, Gonzalez D et al. (2011). Positron emission tomography imaging and clinical progression in relation to molecular pathology in the first Pittsburgh Compound B positron emission tomography patient with Alzheimer’s disease. Brain 134: 301–317. Kanda T, Ishii K, Uemura T et al. (2008). Comparison of grey matter and metabolic reductions in frontotemporal dementia using FDG-PET and voxel-based morphometric MR studies. Eur J Nucl Med Mol Imaging 35: 2227–2234. Kantarci K, Avula R, Senjem ML et al. (2010). Dementia with Lewy bodies and Alzheimer disease: neurodegenerative patterns characterized by DTI. Neurology 74: 1814–1821.

559

Kantarci K, Lowe V, Przybelski SA et al. (2012a). APOE modifies the association between Abeta load and cognition in cognitively normal older adults. Neurology 78: 232–240. Kantarci K, Lowe VJ, Boeve BF et al. (2012b). Multimodality imaging characteristics of dementia with Lewy bodies. Neurobiol Aging 33: 2091–2105. Kantarci K, Weigand SD, Przybelski SA et al. (2013). MRI and MRS predictors of mild cognitive impairment in a population-based sample. Neurology 81: 126–133. Kenny ER, Blamire AM, Firbank MJ et al. (2012). Functional connectivity in cortical regions in dementia with Lewy bodies and Alzheimer’s disease. Brain 135: 569–581. Kerklaan BJ, van Berckel BN, Herholz K et al. (2014). The added value of 18-fluorodeoxyglucose-positron emission tomography in the diagnosis of the behavioral variant of frontotemporal dementia. Am J Alzheimers Dis Other Demen 29: 607–613. Kirkpatrick J, Hayman L (1987). White-matter lesions on MR imaging of clinically healthy brains of elderly subjects: possible pathologic basis. Radiology 162: 509–511. Kirshner HS (2012). Primary progressive aphasia and Alzheimer’s disease: brief history, recent evidence. Curr Neurol Neurosci Rep 12: 709–714. Kiuchi K, Morikawa M, Taoka T et al. (2009). Abnormalities of the uncinate fasciculus and posterior cingulate fasciculus in mild cognitive impairment and early Alzheimer’s disease: a diffusion tensor tractography study. Brain Res 1287: 184–191. Kloppel S, Stonnington CM, Barnes J et al. (2008). Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method. Brain 131: 2969–2974. Knight WD, Kim LG, Douiri A et al. (2011). Acceleration of cortical thinning in familial Alzheimer’s disease. Neurobiol Aging 32: 1765–1773. Knopman DS, DeKosky ST, Cummings JL et al. (2001). Practice parameter: diagnosis of dementia (an evidencebased review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 56: 1143–1153. Knopman DS, Jack Jr CR, Wiste HJ et al. (2012). Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease. Neurology 78: 1576–1582. Knopman DS, Jack Jr CR, Wiste HJ et al. (2013a). Brain injury biomarkers are not dependent on beta-amyloid in normal elderly. Ann Neurol 73: 472–480. Knopman DS, Jack Jr CR, Wiste HJ et al. (2013b). Selective worsening of brain injury biomarker abnormalities in cognitively normal elderly persons with beta-amyloidosis. JAMA Neurol 70: 1030–1038. Kobylecki C, Langheinrich T, Hinz R et al. (2015). 18Fflorbetapir PET in patients with frontotemporal dementia and Alzheimer disease. J Nucl Med 56: 386–391. Koerte IK, Lin AP, Willems A et al. (2015). A review of neuroimaging findings in repetitive brain trauma. Brain Pathol 25: 318–349. Koivunen J, Scheinin N, Virta JR et al. (2011). Amyloid PET imaging in patients with mild cognitive impairment. A 2-year follow-up study. Neurology 76: 1085–1090.

560

J.C. MASDEU AND B. PASCUAL

Kreisl WC, Fujita M, Fujimura Y et al. (2010). Comparison of [(11)C]-(R)-PK 11195 and [(11)C]PBR28, two radioligands for translocator protein (18 kDa) in human and monkey: implications for positron emission tomographic imaging of this inflammation biomarker. Neuroimage 49: 2924–2932. Kreisl WC, Jenko KJ, Hines CS et al. (2013a). A genetic polymorphism for translocator protein 18 kDa affects both in vitro and in vivo radioligand binding in human brain to this putative biomarker of neuroinflammation. J Cereb Blood Flow Metab 33: 53–58. Kreisl WC, Lyoo CH, McGwier M et al. (2013b). In vivo radioligand binding to translocator protein correlates with severity of Alzheimer’s disease. Brain 136: 2228–2238. Kropholler MA, Boellaard R, van Berckel BNM et al. (2007). Evaluation of reference regions for (R)-C-11 PK11195 studies in Alzheimer’s disease and mild cognitive impairment. J Cereb Blood Flow Metab 27: 1965–1974. La Joie R, Perrotin A, Barre L et al. (2012). Region-specific hierarchy between atrophy, hypometabolism, and betaamyloid (Abeta) load in Alzheimer’s disease dementia. J Neurosci 32: 16265–16273. Lam BY, Halliday GM, Irish M et al. (2014). Longitudinal white matter changes in frontotemporal dementia subtypes. Hum Brain Mapp 35: 3547–3557. Lambert JC, Ibrahim-Verbaas CA, Harold D et al. (2013). Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45: 1452–1458. Landau SM, Marks SM, Mormino EC et al. (2012). Association of lifetime cognitive engagement and low beta-amyloid deposition. Arch Neurol 69: 623–629. Landau SM, Thomas BA, Thurfjell L et al. (2014). Amyloid PET imaging in Alzheimer’s disease: a comparison of three radiotracers. Eur J Nucl Med Mol Imaging 41: 1398–1407. Lehmann M, Ghosh PM, Madison C et al. (2013a). Diverging patterns of amyloid deposition and hypometabolism in clinical variants of probable Alzheimer’s disease. Brain 136: 844–858. Lehmann M, Madison CM, Ghosh PM et al. (2013b). Intrinsic connectivity networks in healthy subjects explain clinical variability in Alzheimer’s disease. Proc Natl Acad Sci U S A 110: 11606–11611. Leyton CE, Villemagne VL, Savage S et al. (2011). Subtypes of progressive aphasia: application of the International Consensus Criteria and validation using beta-amyloid imaging. Brain 134: 3030–3043. Lim SM, Katsifis A, Villemagne VL et al. (2009). The 18 F-FDG PET cingulate island sign and comparison to 123I-beta-CIT SPECT for diagnosis of dementia with Lewy bodies. J Nucl Med 50: 1638–1645. Lim YY, Villemagne VL, Laws SM et al. (2013). BDNF Val66Met, Abeta amyloid, and cognitive decline in preclinical Alzheimer’s disease. Neurobiol Aging 34: 2457–2464. Liscic RM, Storandt M, Cairns NJ et al. (2007). Clinical and psychometric distinction of frontotemporal and Alzheimer dementias. Arch Neurol 64: 535–540. Liu L, Drouet V, Wu JW et al. (2012). Trans-synaptic spread of tau pathology in vivo. PLoS One 7: e31302.

Logothetis NK (2008). What we can do and what we cannot do with fMRI. Nature 453: 869–878. Luk KC, Kehm V, Carroll J et al. (2012). Pathological alphasynuclein transmission initiates Parkinson-like neurodegeneration in nontransgenic mice. Science 338: 949–953. Machulda MM, Jones DT, Vemuri P et al. (2011). Effect of APOE epsilon4 status on intrinsic network connectivity in cognitively normal elderly subjects. Arch Neurol 68: 1131–1136. Madhavan A, Whitwell JL, Weigand SD et al. (2013). FDG PET and MRI in logopenic primary progressive aphasia versus dementia of the Alzheimer’s type. PLoS One 8: e62471. Maeda J, Zhang MR, Okauchi T et al. (2011). In vivo positron emission tomographic imaging of glial responses to amyloid-beta and tau pathologies in mouse models of Alzheimer’s disease and related disorders. J Neurosci 31: 4720–4730. Mahoney CJ, Ridgway GR, Malone IB et al. (2014). Profiles of white matter tract pathology in frontotemporal dementia. Hum Brain Mapp 35: 4163–4179. Mahoney CJ, Simpson IJ, Nicholas JM et al. (2015). Longitudinal diffusion tensor imaging in frontotemporal dementia. Ann Neurol 77: 33–46. Mandelli ML, Caverzasi E, Binney RJ et al. (2014). Frontal white matter tracts sustaining speech production in primary progressive aphasia. J Neurosci 34: 9754–9767. Marchant NL, Reed BR, Sanossian N et al. (2013). The aging brain and cognition: contribution of vascular injury and abeta to mild cognitive dysfunction. JAMA Neurol 70: 488–495. Maruyama M, Shimada H, Suhara T et al. (2013). Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron 79: 1094–1108. Masdeu J (2008). Neuroimaging of disorders leading to dementia. Continuum (AAN) 14: 144–163. Masdeu JC, Arbizu J (2008). Brain single photon emission computed tomography: technological aspects and clinical applications. Semin Neurol 28: 423–434. Masdeu JC, Zubieta JL, Arbizu J (2005). Neuroimaging as a marker of the onset and progression of Alzheimer’s disease. J Neurol Sci 236: 55–64. Masdeu JC, Kreisl WC, Berman KF (2012). The neurobiology of Alzheimer disease defined by neuroimaging. Curr Opin Neurol 25: 410–420. Massey LA, Jager HR, Paviour DC et al. (2013). The midbrain to pons ratio: a simple and specific MRI sign of progressive supranuclear palsy. Neurology 80: 1856–1861. Mathis CA, Kuller LH, Klunk WE et al. (2013). In vivo assessment of amyloid-beta deposition in nondemented very elderly subjects. Ann Neurol 73: 751–761. McEvoy LK, Fennema-Notestine C, Roddey JC et al. (2009). Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 251: 195–205. McKee AC, Stein TD, Nowinski CJ et al. (2013). The spectrum of disease in chronic traumatic encephalopathy. Brain 136: 43–64.

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA McKeith IG, Dickson DW, Lowe J et al. (2005). Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 65: 1863–1872. McKhann GM, Knopman DS, Chertkow H et al. (2011). The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on AgingAlzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7: 263–269. McMillan CT, Brun C, Siddiqui S et al. (2012). White matter imaging contributes to the multimodal diagnosis of frontotemporal lobar degeneration. Neurology 78: 1761–1768. Mendez MF, Shapira JS, McMurtray A et al. (2007). Accuracy of the clinical evaluation for frontotemporal dementia. Arch Neurol 64: 830–835. Mesulam MM, Weintraub S, Rogalski EJ et al. (2014). Asymmetry and heterogeneity of Alzheimer’s and frontotemporal pathology in primary progressive aphasia. Brain 137: 1176–1192. Mielke MM, Kozauer NA, Chan KC et al. (2009). Regionallyspecific diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neuroimage 46: 47–55. Miettinen PS, Pihlajamaki M, Jauhiainen AM et al. (2011). Structure and function of medial temporal and posteromedial cortices in early Alzheimer’s disease. Eur J Neurosci 34: 320–330. Miller ZA, Rankin KP, Graff-Radford NR et al. (2013). TDP43 frontotemporal lobar degeneration and autoimmune disease. J Neurol Neurosurg Psychiatry 84: 956–962. Mondadori CR, de Quervain DJ, Buchmann A et al. (2007). Better memory and neural efficiency in young apolipoprotein E epsilon4 carriers. Cereb Cortex 17: 1934–1947. Monsell SE, Mock C, Roe CM et al. (2013). Comparison of symptomatic and asymptomatic persons with Alzheimer disease neuropathology. Neurology 80: 2121–2129. Montine TJ, Phelps CH, Beach TG et al. (2012). National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach. Acta Neuropathol 123: 1–11. Mormino EC, Kluth JT, Madison CM et al. (2009). Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain 132: 1310–1323. Morra JH, Tu Z, Apostolova LG et al. (2009). Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Neuroimage 45: S3–S15. Morris JC (2012). Revised criteria for mild cognitive impairment may compromise the diagnosis of Alzheimer disease dementia. Arch Neurol 69: 700–708. Mosconi L, Brys M, Glodzik-Sobanska L et al. (2007). Early detection of Alzheimer’s disease using neuroimaging. Exp Gerontol 42: 129–138. Mosconi L, Tsui WH, Herholz K et al. (2008). Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med 49: 390–398.

561

Mueller SG, Weiner MW (2009). Selective effect of age, Apo e4, and Alzheimer’s disease on hippocampal subfields. Hippocampus 19: 558–564. Nedelska Z, Ferman TJ, Boeve BF et al. (2015). Pattern of brain atrophy rates in autopsy-confirmed dementia with Lewy bodies. Neurobiol Aging 36: 452–461. Nelson PT, Head E, Schmitt FA et al. (2011). Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies. Acta Neuropathol 121: 571–587. Nelson PT, Alafuzoff I, Bigio EH et al. (2012). Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol 71: 362–381. Nichols LM, Masdeu JC, Mattay VS et al. (2012). Interactive effect of apolipoprotein e genotype and age on hippocampal activation during memory processing in healthy adults. Arch Gen Psychiatry 69: 804–813. Nolan KA, Lino MM, Seligmann AW et al. (1998). Absence of vascular dementia in an autopsy series from a dementia clinic. J Am Geriatr Soc 46: 597–604. O’Brien JL, O’Keefe KM, LaViolette PS et al. (2010). Longitudinal fMRI in elderly reveals loss of hippocampal activation with clinical decline. Neurology 74: 1969–1976. O’Brien JT, Firbank MJ, Davison C et al. (2014). 18F-FDG PET and perfusion SPECT in the diagnosis of Alzheimer and Lewy body dementias. J Nucl Med 55: 1959–1965. O’Dwyer L, Lamberton F, Bokde AL et al. (2012). Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS One 7: e32441. Okello A, Edison P, Archer HA et al. (2009a). Microglial activation and amyloid deposition in mild cognitive impairment: a PET study. Neurology 72: 56–62. Okello A, Koivunen J, Edison P et al. (2009b). Conversion of amyloid positive and negative MCI to AD over 3 years: an 11 C-PIB PET study. Neurology 73: 754–760. Ossenkoppele R, Tolboom N, Foster-Dingley JC et al. (2012). Longitudinal imaging of Alzheimer pathology using [(11) C]PIB, [(18)F]FDDNP and [(18)F]FDG PET. Eur J Nucl Med Mol Imaging 39: 990–1000. Ossenkoppele R, Schonhaut DR, Baker SL et al. (2015). Tau, amyloid, and hypometabolism in a patient with posterior cortical atrophy. Ann Neurol 77: 338–342. Ostrowitzki S, Deptula D, Thurfjell L et al. (2012). Mechanism of amyloid removal in patients with Alzheimer disease treated with gantenerumab. Arch Neurol 69: 198–207. Owen DR, Yeo AJ, Gunn RN et al. (2012). An 18-kDa translocator protein (TSPO) polymorphism explains differences in binding affinity of the PET radioligand PBR28. J Cereb Blood Flow Metab 32: 1–5. Pascual B, Prieto E, Arbizu J et al. (2010). Brain glucose metabolism in vascular white matter disease with dementia: differentiation from Alzheimer disease. Stroke 41: 2889–2893. Pascual B, Prieto E, Arbizu J et al. (2012a). Decreased carbon11-flumazenil binding in early Alzheimer’s disease. Brain 135: 2817–2825.

562

J.C. MASDEU AND B. PASCUAL

Pascual B, Prieto E, Marti-Climent J et al. (2012b). Decreased 11C-flumazenil binding in early Alzheimer disease. J Neuroimaging 22: 106. Perez-Nievas BG, Stein TD, Tai HC et al. (2013). Dissecting phenotypic traits linked to human resilience to Alzheimer’s pathology. Brain 136: 2510–2526. Perrotin A, Mormino EC, Madison CM et al. (2012). Subjective cognition and amyloid deposition imaging: a Pittsburgh Compound B positron emission tomography study in normal elderly individuals. Arch Neurol 69: 223–229. Petersen RC (2004). Dementia. Lippincott Williams & Wilkins, Baltimore, MD. Petersen RC (2013). Do preclinical Alzheimer’s disease criteria work? Lancet Neurol 12: 933–935. Petersen RC, Aisen P, Boeve BF et al. (2013). Mild cognitive impairment due to Alzheimer disease in the community. Ann Neurol 74: 199–208. Piolino P, Chetelat G, Matuszewski V et al. (2007). In search of autobiographical memories: a PET study in the frontal variant of frontotemporal dementia. Neuropsychologia 45: 2730–2743. Power JD, Barnes KA, Snyder AZ et al. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59: 2142–2154. Prusiner SB (2013). Biology and genetics of prions causing neurodegeneration. Annu Rev Genet 47: 601–623. Putcha D, Brickhouse M, O’Keefe K et al. (2011). Hippocampal hyperactivation associated with cortical thinning in Alzheimer’s disease signature regions in non-demented elderly adults. J Neurosci 31: 17680–17688. Quiroz YT, Budson AE, Celone K et al. (2010). Hippocampal hyperactivation in presymptomatic familial Alzheimer’s disease. Ann Neurol 68: 865–875. Rabinovici GD, Furst AJ, O’Neil JP et al. (2007). 11C-PIB PET imaging in Alzheimer disease and frontotemporal lobar degeneration. Neurology 68: 1205–1212. Rabinovici GD, Jagust WJ, Furst AJ et al. (2008). Abeta amyloid and glucose metabolism in three variants of primary progressive aphasia. Ann Neurol 64: 388–401. Rabinovici GD, Rosen HJ, Alkalay A et al. (2011). Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD. Neurology 77: 2034–2042. Ramlackhansingh AF, Brooks DJ, Greenwood RJ et al. (2011). Inflammation after trauma: microglial activation and traumatic brain injury. Ann Neurol 70: 374–383. Rascovsky K, Hodges JR, Knopman D et al. (2011). Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134: 2456–2477. Reijmer YD, van Veluw SJ, Greenberg SM (2015). Ischemic brain injury in cerebral amyloid angiopathy. J Cereb Blood Flow Metab. http://dx.doi.org/10.1038/jcbfm.2015.88 (epub ahead of print). Reiman EM, Caselli RJ, Yun LS et al. (1996). Preclinical evidence of Alzheimer’s disease in persons homozygous for the epsilon 4 allele for apolipoprotein E. N Engl J Med 334: 752–758.

Reiman EM, Chen K, Alexander GE et al. (2004). Functional brain abnormalities in young adults at genetic risk for lateonset Alzheimer’s dementia. Proc Natl Acad Sci U S A 101: 284–289. Ringman JM, O’Neill J, Geschwind D et al. (2007). Diffusion tensor imaging in preclinical and presymptomatic carriers of familial Alzheimer’s disease mutations. Brain 130: 1767–1776. Ringman JM, Pope W, Salamon N (2010). Insensitivity of visual assessment of hippocampal atrophy in familial Alzheimer’s disease. J Neurol 257: 839–842. Ringman JM, Medina LD, Braskie M et al. (2011). Effects of risk genes on BOLD activation in presymptomatic carriers of familial Alzheimer’s disease mutations during a novelty encoding task. Cereb Cortex 21: 877–883. Rocher AB, Chapon F, Blaizot X et al. (2003). Resting-state brain glucose utilization as measured by PET is directly related to regional synaptophysin levels: a study in baboons. Neuroimage 20: 1894–1898. Roman GC, Tatemichi TK, Erkinjuntti T et al. (1993). Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 43: 250–260. Rosenbloom MH, Alkalay A, Agarwal N et al. (2011). Distinct clinical and metabolic deficits in PCA and AD are not related to amyloid distribution. Neurology 76: 1789–1796. Rowe CC, Villemagne VL (2011). Brain amyloid imaging. J Nucl Med 52: 1733–1740. Rowe CC, Ng S, Ackermann U et al. (2007). Imaging betaamyloid burden in aging and dementia. Neurology 68: 1718–1725. Rowe CC, Pejoska S, Mulligan RS et al. (2013). Head-to-head comparison of 11C-PiB and 18F-AZD4694 (NAV4694) for beta-amyloid imaging in aging and dementia. J Nucl Med 54: 880–886. Sabri O, Sabbagh MN, Seibyl J et al. (2015). Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer disease: phase 3 study. Alzheimers Dement 11: 964–974. Sajjadi SA, Acosta-Cabronero J, Patterson K et al. (2013). Diffusion tensor magnetic resonance imaging for single subject diagnosis in neurodegenerative diseases. Brain 136: 2253–2261. Salmon E, Garraux G, Delbeuck X et al. (2003). Predominant ventromedial frontopolar metabolic impairment in frontotemporal dementia. Neuroimage 20: 435–440. Sanchez-Juan P, Ghosh PM, Hagen J et al. (2014). Practical utility of amyloid and FDG-PET in an academic dementia center. Neurology 82: 230–238. Santillo AF, Martensson J, Lindberg O et al. (2013). Diffusion tensor tractography versus volumetric imaging in the diagnosis of behavioral variant frontotemporal dementia. PLoS One 8: e66932. Sapolsky D, Bakkour A, Negreira A et al. (2010). Cortical neuroanatomic correlates of symptom severity in primary progressive aphasia. Neurology 75: 358–366. Savva GM, Wharton SB, Ince PG et al. (2009). Age, neuropathology, and dementia. N Engl J Med 360: 2302–2309.

GENETIC AND DEGENERATIVE DISORDERS PRIMARILY CAUSING DEMENTIA Scheltens P, Fox N, Barkhof F et al. (2002). Structural magnetic resonance imaging in the practical assessment of dementia: beyond exclusion. Lancet Neurol 1: 13–21. Schneider JA, Arvanitakis Z, Bang W et al. (2007). Mixed brain pathologies account for most dementia cases in communitydwelling older persons. Neurology 69: 2197–2204. Schuitemaker A, Kropholler MA, Boellaard R et al. (2013). Microglial activation in Alzheimer’s disease: an (R)-[(1) (1)C]PK11195 positron emission tomography study. Neurobiol Aging 34: 128–136. Seeley WW, Crawford RK, Zhou J et al. (2009). Neurodegenerative diseases target large-scale human brain networks. Neuron 62: 42–52. Sepulcre J, Masdeu JC (2015). Advanced neuroimaging methods towards characterization of early stages of AD. In: JI Castrillo, SG Oliver (Eds.), Systems Biology of Alzheimer’s Disease. Humana Press, London. Sepulcre J, Sabuncu MR, Johnson KA (2012). Network assemblies in the functional brain. Curr Opin Neurol 25: 384–391. Serrano-Pozo A, Mielke ML, Gomez-Isla T et al. (2011). Reactive glia not only associates with plaques but also parallels tangles in Alzheimer’s disease. Am J Pathol 179: 1373–1384. Sheline YI, Morris JC, Snyder AZ et al. (2010). APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Abeta42. J Neurosci 30: 17035–17040. Shin J, Lee SY, Kim SJ et al. (2010). Voxel-based analysis of Alzheimer’s disease PET imaging using a triplet of radiotracers: PIB, FDDNP, and FDG. Neuroimage 52: 488–496. Shin J, Kepe V, Barrio JR et al. (2011). The merits of FDDNPPET imaging in Alzheimer’s disease. J Alzheimers Dis 26: 135–145. Silverman DH (2004). Brain 18F-FDG PET in the diagnosis of neurodegenerative dementias: comparison with perfusion SPECT and with clinical evaluations lacking nuclear imaging. J Nucl Med 45: 594–607. Silverman DH, Small GW, Chang CY et al. (2001). Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA 286: 2120–2127. Small GW, Siddarth P, Kepe V et al. (2012). Prediction of cognitive decline by positron emission tomography of brain amyloid and tau. Arch Neurol 69: 215–222. Smith CD, Chebrolu H, Andersen AH et al. (2010). White matter diffusion alterations in normal women at risk of Alzheimer’s disease. Neurobiol Aging 31: 1122–1131. Sperling RA, Dickerson BC, Pihlajamaki M et al. (2010). Functional alterations in memory networks in early Alzheimer’s disease. Neuromolecular Med 12: 27–43. Sperling RA, Aisen PS, Beckett LA et al. (2011a). Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7: 280–292. Sperling RA, Jack Jr CR, Black SE et al. (2011b). Amyloidrelated imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer’s Association Research Roundtable Workgroup. Alzheimers Dement 7: 367–385.

563

Sperling R, Salloway S, Brooks DJ et al. (2012). Amyloidrelated imaging abnormalities in patients with Alzheimer’s disease treated with bapineuzumab: a retrospective analysis. Lancet Neurol 11: 241–249. Sperling RA, Rentz DM, Johnson KA et al. (2014). The A4 study: stopping AD before symptoms begin? Sci Transl Med 6: 228fs213. Stern RA, Daneshvar DH, Baugh CM et al. (2013). Clinical presentation of chronic traumatic encephalopathy. Neurology 81: 1122–1129. Stricker NH, Schweinsburg BC, Delano-Wood L et al. (2009). Decreased white matter integrity in late-myelinating fiber pathways in Alzheimer’s disease supports retrogenesis. Neuroimage 45: 10–16. Teune LK, Bartels AL, de Jong BM et al. (2010). Typical cerebral metabolic patterns in neurodegenerative brain diseases. Mov Disord 25: 2395–2404. Thiele F, Young S, Buchert R et al. (2013). Voxel-based classification of FDG PET in dementia using inter-scanner normalization. Neuroimage 77: 62–69. Thompson PM, Hayashi KM, de Zubicaray G et al. (2003). Dynamics of gray matter loss in Alzheimer’s disease. J Neurosci 23: 994–1005. Tolboom N, van der Flier WM, Boverhoff J et al. (2010). Molecular imaging in the diagnosis of Alzheimer’s disease: visual assessment of (11)C PIB and (18)F FDDNP PET images. J Neurol Neurosurg Psychiatry 81: 882–884. Trivedi MA, Schmitz TW, Ries ML et al. (2008). fMRI activation during episodic encoding and metacognitive appraisal across the lifespan: risk factors for Alzheimer’s disease. Neuropsychologia 46: 1667–1678. Urs R, Potter E, Barker W et al. (2009). Visual rating system for assessing magnetic resonance images: a tool in the diagnosis of mild cognitive impairment and Alzheimer disease. J Comput Assist Tomogr 33: 73–78. van der Flier WM, van Straaten EC, Barkhof F et al. (2005). Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke 36: 2116–2120. Vellas B, Carrillo MC, Sampaio C et al. (2013). Designing drug trials for Alzheimer’s disease: what we have learned from the release of the phase III antibody trials: a report from the EU/US/CTAD Task Force. Alzheimers Dement 9: 438–444. Vemuri P, Whitwell JL, Kantarci K et al. (2008). Antemortem MRI based STructural Abnormality iNDex (STAND)scores correlate with postmortem Braak neurofibrillary tangle stage. Neuroimage 42: 559–567. Vemuri P, Lesnick TG, Przybelski SA et al. (2012). Effect of lifestyle activities on Alzheimer disease biomarkers and cognition. Ann Neurol 72: 730–738. Vemuri P, Lesnick TG, Przybelski SA et al. (2015). Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly. Brain 138: 761–771. Villemagne VL, Ong K, Mulligan RS et al. (2011a). Amyloid imaging with (18)F-florbetaben in Alzheimer disease and other dementias. J Nucl Med 52: 1210–1217. Villemagne VL, Pike KE, Chetelat G et al. (2011b). Longitudinal assessment of Abeta and cognition in aging and Alzheimer disease. Ann Neurol 69: 181–192.

564

J.C. MASDEU AND B. PASCUAL

Villemagne VL, Burnham S, Bourgeat P et al. (2013). Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol 12: 357–367. Villemagne VL, Fodero-Tavoletti MT, Masters CL et al. (2015). Tau imaging: early progress and future directions. Lancet Neurol 14: 114–124. Viswanathan A, Gschwendtner A, Guichard JP et al. (2007). Lacunar lesions are independently associated with disability and cognitive impairment in CADASIL. Neurology 69: 172–179. Vlassenko AG, Mintun MA, Xiong C et al. (2011). Amyloidbeta plaque growth in cognitively normal adults: longitudinal [11C]Pittsburgh compound B data. Ann Neurol 70: 857–861. Vos SJ, Xiong C, Visser PJ et al. (2013). Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study. Lancet Neurol 12: 957–965. Wang L, Khan A, Csernansky JG et al. (2009). Fullyautomated, multi-stage hippocampus mapping in very mild Alzheimer disease. Hippocampus 19: 541–548. Wee CY, Yap PT, Zhang D et al. (2012). Identification of MCI individuals using structural and functional connectivity networks. Neuroimage 59: 2045–2056. Whitwell JL, Josephs KA, Murray ME et al. (2008). MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study. Neurology 71: 743–749. Whitwell JL, Przybelski SA, Weigand SD et al. (2009). Distinct anatomical subtypes of the behavioural variant of frontotemporal dementia: a cluster analysis study. Brain 132: 2932–2946. Whitwell JL, Avula R, Senjem ML et al. (2010). Gray and white matter water diffusion in the syndromic variants of frontotemporal dementia. Neurology 74: 1279–1287. Whitwell JL, Dickson DW, Murray ME et al. (2012a). Neuroimaging correlates of pathologically defined subtypes of Alzheimer’s disease: a case-control study. Lancet Neurol 11: 868–877. Whitwell JL, Weigand SD, Boeve BF et al. (2012b). Neuroimaging signatures of frontotemporal dementia genetics: C9ORF72, tau, progranulin and sporadics. Brain 135: 794–806. Whitwell JL, Jack Jr CR, Parisi JE et al. (2013). Midbrain atrophy is not a biomarker of progressive supranuclear palsy pathology. Eur J Neurol 20: 1417–1422.

Whitwell JL, Boeve BF, Weigand SD et al. (2015a). Brain atrophy over time in genetic and sporadic frontotemporal dementia: a study of 198 serial magnetic resonance images. Eur J Neurol 22: 745–752. Whitwell JL, Duffy JR, Strand EA et al. (2015b). Clinical and neuroimaging biomarkers of amyloid-negative logopenic primary progressive aphasia. Brain Lang 142: 45–53. Whitwell JL, Jones DT, Duffy JR et al. (2015c). Working memory and language network dysfunctions in logopenic aphasia: a task-free fMRI comparison with Alzheimer’s dementia. Neurobiol Aging 36: 1245–1252. Wolk DA, Price JC, Saxton JA et al. (2009). Amyloid imaging in mild cognitive impairment subtypes. Ann Neurol 65: 557–568. Wolk DA, Grachev ID, Buckley C et al. (2011). Association between in vivo fluorine 18-labeled flutemetamol amyloid positron emission tomography imaging and in vivo cerebral cortical histopathology. Arch Neurol 68: 1398–1403. Wolz R, Julkunen V, Koikkalainen J et al. (2011). Multimethod analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One 6: e25446. Yasuno F, Kosaka J, Ota M et al. (2012). Increased binding of peripheral benzodiazepine receptor in mild cognitive impairment-dementia converters measured by positron emission tomography with [(1)(1)C]DAA1106. Psychiatry Res 203: 67–74. Yoder KK, Nho K, Risacher SL et al. (2013). Influence of TSPO genotype on 11C-PBR28 standardized uptake values. J Nucl Med 54: 1320–1322. Yuan Y, Gu ZX, Wei WS (2009). Fluorodeoxyglucosepositron-emission tomography, single-photon emission tomography, and structural MR imaging for prediction of rapid conversion to Alzheimer disease in patients with mild cognitive impairment: a meta-analysis. AJNR Am J Neuroradiol 30: 404–410. Zhang Y, Schuff N, Du AT et al. (2009). White matter damage in frontotemporal dementia and Alzheimer’s disease measured by diffusion MRI. Brain 132: 2579–2592. Zhang Y, Tartaglia MC, Schuff N et al. (2013). MRI signatures of brain macrostructural atrophy and microstructural degradation in frontotemporal lobar degeneration subtypes. J Alzheimers Dis 33: 431–444. Zhou J, Gennatas ED, Kramer JH et al. (2012). Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73: 1216–1227.