Neuroimaging Findings in Mild Cognitive Impairment

Clinical workout for the early detection of cognitive decline and dementia

Article 01 October 2014

Biomarkers for Alzheimer’s Disease and Frontotemporal Lobar Degeneration: Imaging

Keywords

1 Introduction

The clinical construct of mild cognitive impairment (MCI), put forward at the end of the 1990s (Petersen et al. 1999), has bridged the gap between benign forgetfulness due to aging and dementia, a difficult to be interpreted “gray” area and sometimes generating confusion in literature as well as in clinical practice. During the last decades, the efforts to elucidate the pathophysiological bases of MCI have matched the change in the conception of neurodegenerative diseases. Thus, the traditional diagnostic methods based only on clinical presentation, and eventually confirmed by postmortem neuropathological findings, have been replaced by a biomarker-based approach involving different biological measures able to reflect pathological changes in vivo.

Such approach is pivotal in understanding the underlying processes and in giving an insight in their pathological etiology, thus guiding clinicians in the differential diagnostic pathway of MCI as well as researchers in the development of new therapies. The MCI syndrome has been further classified, according to the cognitive profile of impairment, into “amnestic” (aMCI) and “non-amnestic” (naMCI) forms, with isolated (single-domain) or multiple (multi-domain) cognitive deficits (Petersen and Negash 2008). However, neuropsychological, cerebrospinal fluid (CSF), genetics, and neuroimaging studies have clarified that MCI is a clinical-neuropsychological syndrome and not a disease entity, as it may be underpinned by a myriad of neurological, psychiatric, and systemic disorders. In particular, one of the most attractive opportunities offered by the MCI construct is that it allows to track neurodegenerative disorders before the stage of dementia. Alzheimer’s disease (AD) is the most common neurodegenerative disease of the elderly population and an aMCI syndrome, typically an episodic memory deficit not helped by semantic cues, antedates the onset of dementia by years. Instead, in the MCI stage of AD with presenile onset (i.e., before 65 years), memory function is often relatively spared and a neocortical deficit including apraxia, agnosia, aphasia, or their various combination, dominates the cognitive deficit. On the other hand, dementia with Lewy bodies (DLB), frontotemporal lobe degeneration (FTLD), and vascular cognitive impairment (VCI) patients more often exhibit a naMCI syndrome, although exceptions to this rule are frequent (Petersen and Negash 2008). Finally, depression, which is often under-detected in the elderly, further complicates the differential diagnosis, as it may present with either aMCI or naMCI (Ismail et al. 2017). Thus, the same neuropsychological profile considerably overlaps among different diseases causing cognitive impairment.

Within this framework, biomarkers have confirmed their relevance in understanding the pathophysiology which underpins cognitive deficit, thus paving the way to the correct diagnosis. The bulk of evidence has become so convincing that the proposed new lexicon for AD calls “prodromal” AD those MCI patients with firm biological (i.e., neuroimaging and/or CSF) evidence of the AD-neurodegenerative process and leaves the term MCI to collect all those cases in whom a definite evidence of AD or other specific processes is lacking (Dubois et al. 2010). Thus, nowadays, the definition of AD in living people is shifting from a syndromic to a biological construct, in which the cardinal neuropathological features must be present, i.e., amyloidosis and tauopathy, while signs of neurodegeneration and even more clinical symptoms may or may not coexist. Thus, even if β amyloid plaques and neurofibrillary tau deposits might not be causal in AD pathogenesis, these abnormal protein deposits define AD as a unique neurodegenerative disease among different disorders leading to dementia (Jack et al. 2018). Similarly, in all the other neurodegenerative diseases, a biological rather than a syndromic approach has the goal to find unique neuropathological features and thus to distinguish one with the respect to another at the MCI stage. However, the clinical validation of all these biomarkers is still incomplete and needs further studies in longitudinal cohorts with adequate follow-up and pathological confirmation, with the evaluation of the impact they have on prognosis and outcome (Frisoni et al. 2017).

Structural neuroimaging by means of computed tomography (CT) opened the way to this concept, but after a while, it was replaced by magnetic resonance imaging (MRI) that nowadays is commonly performed by means of 1.5 T equipment but almost replaced by 3 T equipment in most advanced hospitals in the Western world, and experimental studies are now using 7 T equipment. MRI has led to a tremendous improvement of morphological imaging, helping the diagnostic procedure by the evidence of different topographical patterns of atrophy, vascular damage, and microbleeds/superficial siderosis. More recently, new MRI techniques have allowed to study the integrity of white matter fibers (diffusion tensor imaging, DTI), expanding the focus of the impact of neurodegenerative and cerebrovascular diseases at this level, too. Magnetic resonance is also able to investigate brain function during administration of cognitive paradigms. By means of “blood-oxygen-level-dependent” (BOLD) technique, increases in regional cerebral blood flow (rCBF) can be detected and mapped with “functional MRI” (fMRI), showing different patterns of activation between MCI subjects and healthy controls, who also differ in the so-called default mode network (DMN) of brain functioning as emerged from “resting-state” fMRI (Greicius et al. 2004).

Functional neuroimaging has become widely used thanks to perfusion ^99mTc radiopharmaceuticals and SPECT technology that have largely contributed to our knowledge in dementia and then in MCI since the early 1990s. Peculiar dysfunction pictures in major brain regions for pre-dementia AD and FTLD have been identified, followed by interesting data in DLB. However, as spatial resolution is only about 1 cm or just a bit better (typically 7–8 mm), SPECT technology has been progressively replaced by PET technology, which ensure a much higher spatial resolution (typically about 3 mm), thus further improving detection of functional impairment even in small brain structures. Nowadays, ¹⁸F-fluorodeoxyglucose PET (FDG-PET) is the cornerstone of brain functional assessment, but in the last years, tracers for brain amyloid load have opened a new field (Herholz and Ebmeier 2011), and new tracers for tau imaging are in an advanced stage of development (Leuzy et al. 2019). Moreover, neuroreceptor systems can be visualized by injecting specific radiopharmaceuticals binding to dopamine, acetylcholine, and serotonin receptors or transporters, and other tracers allow imaging of inflammation and synaptic function. The majority of these last tracers are still labeled with ¹¹C, which forces research centers to produce them in situ, while fluorinated (i.e., ¹⁸F-labeled) transportable compounds are still limited in number.

In this chapter, major neuroimaging-provided clues to the understanding, classification, and clinical diagnosis of the composite MCI syndrome are critically reviewed.

2 Morphological MRI

2.1 Principles

Structural magnetic resonance imaging (MRI) is recognized as an important diagnostic tool for AD. Since it is one of the most widespread tools in the clinical practice, it can be used to assess in vivo the structural brain changes noninvasively and relatively cheaply. Thanks to these characteristics and the fact that clinical scanners have been around for some decades, MRI has become a dependable support in diagnosing early and prodromal AD which is important for both an appropriate patient management and a quick support to relatives and caregivers. Moreover, morphological MRI is important to exclude structural lesions potentially caused by tumors, subdural hematoma, arteriovenous malformations, and hydrocephalus (Harper et al. 2014). In addition, pathophysiological studies confirmed that cerebral atrophy and white matter changes in the living brain reflect underlying neuropathology and may be detectable using ante mortem MRI.

In AD, the neurobiological changes occur years before the onset of symptoms and are defined by the abnormal deposition of fibrillar amyloid-β and tau aggregates in the brain tissue leading to a progressive synaptic, neuronal, and axonal damage. Despite its lack of specificity for AD, the downstream neurodegeneration biomarkers, including morphological MRI, are incorporated in the 2018 research framework providing important pathologic staging information (Jack et al. 2018). Neurodegeneration signs associated to AD are observable both in white (WM) and gray (GM) matter (Jack 2012). Particularly, the cumulative loss and shrinkage of the neuropil are reflected in the atrophy measured by MRI (Zarow et al. 2005; Barkhof et al. 2007). Patterns of cortical atrophy may aid the differential diagnosis of neurodegenerative diseases beyond AD, including FTLD, corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and vascular dementia (VaD). However, the overlap between these diseases is consistent, and findings should always be considered along with clinical examination and taking aging into account (Harper et al. 2014).

The need of quantitative MRI processing and visualization derives from the fact that the human eye cannot perceive the subtle anatomical changes affecting the structures of the brain; thus, it detects atrophy after the brain has already undergone irreversible synaptic loss. The detailed spatial patterns of atrophy have been extensively studied, using methods such as voxel-based morphometry (VBM) and surface-based methods (with techniques such as the boundary shift integral) (Fox and Freeborough 1997).

In this context, current research heavily relies on sophisticated analysis techniques (including volume, thickness, shape, and texture analysis—see Table 12.1) where the patterns and the extent of cortical atrophy are key players for an accurate and effective usage of MRI in patients with cognitive impairment.

Table 12.1 Selected quantitative MRI studies in the classification of MCI and AD subject (modified by Leandrou et al. 2018)

Full size table

In AD, GM atrophy is first observed in medial temporal lobe (MTL) structures, especially in the hippocampus and the entorhinal cortex (Pennanen et al. 2004; Du et al. 2004). As the disease progresses, the damage extends to the remaining part of the MTL (Li et al. 2011) (i.e., medial temporal gyrus, parahippocampus, fusiform gyrus, and temporal pole) and then to the extratemporal regions including the posterior cingulate gyrus (PCG)/precuneus and the middle frontal gyrus (Wolk et al. 2011; Irish et al. 2013, 2012; Doré et al. 2013). Additional structures involved in AD are the amygdala, the olfactory bulb tract and the primary olfactory cortex, the putamen, and the basal forebrain cholinergic system; atrophy also spreads to frontal, parietal, and temporal brain areas and to the cerebellum, brainstem, and thalamus (Duarte et al. 2006; de Jong et al. 2008; Thomann et al. 2009; Guo et al. 2010; Cavedo et al. 2011; Kilimann et al. 2014; Lee et al. 2015; Tabatabaei-Jafari et al. 2017; Vasavada et al. 2017).

The hippocampal shrinkage is a key feature of AD (see Fig. 12.1); it is one of the most validated and easily accessed biomarkers of AD, and it has been associated to a variety of memory dysfunctions (Wolk et al. 2011; Doré et al. 2013; Lind et al. 2006; Dowell et al. 2016; Bonner-Jackson et al. 2015; Oosterman et al. 2012; Chetelat et al. 2003; Fujishima et al. 2014; Peter et al. 2016; Deweer et al. 1995; Kramer et al. 2005; Sarazin et al. 2010; Kerchner et al. 2012; Sexton et al. 2010; Molinuevo et al. 2011; Gomar et al. 2017). Several studies showed its effectiveness for demonstrating short-term clinical decline (Landau et al. 2010; Ewers et al. 2012; Lehmann et al. 2013; Jack et al. 2015) and for predicting conversion from the stage of MCI to the one of dementia (Jack et al. 2015; Holland et al. 2012; Leung et al. 2013). Especially, the hippocampal subfields are diversely affected by AD showing different sensitivity and specificity in distinguishing between AD dementia and MCI due to AD, as well as other dementias (La Joie et al. 2013; Li et al. 2013; Maruszak and Thuret 2014; Perrotin et al. 2015). In fact, a growing body of evidence suggests that the CA1 and subiculum subfields have the highest diagnostic power in detecting early AD (de Flores et al. 2015).

The combination of hippocampal and cortical features may also be used to differentiate pathologically defined subtypes of AD. For example, the greatest medial temporal lobe atrophy is observed with limbic-predominant AD, followed by typical AD and then hippocampal-sparing AD; conversely, the greatest cortical atrophy is seen in hippocampal-sparing AD, followed by typical AD and then limbic-predominant AD. So, the best discriminator of the three subtypes of AD is the ratio of hippocampal to cortical volumes (Whitwell et al. 2012).

With the advent of higher magnetic field strength scanners, the MTL remains an important target for imaging assessment at increased spatial resolution. Work at 3 T showed significant improvements due to the higher signal-to-noise ratio (SNR) and human studies at 7 T have again shown that improved SNR achieves greater reliability in visualization of the main hippocampal substructures (Theysohn et al. 2009; Kerchner et al. 2010) and at highest resolution begins to show finer details such as the dentate gyrus cell layer at only 0.1 mm in thickness (Kirov et al. 2013). However, the long duration of the scan and subject motion on a scale commensurate to—or often greater than—the size of the structures of interest hampers routine use of these techniques in clinical populations.

While AD is primarily associated with GM loss, WM may be concomitantly affected. It has been shown that MTL WM volume and integrity are positively related to memory performance in amnestic MCI (aMCI) and AD dementia (Sexton et al. 2010; Stoub et al. 2006; Zhuang et al. 2012; Meyer et al. 2013; Yakushev et al. 2010). Moreover, in APOE ε4 carriers, loss of entorhinal WM integrity was related to worse memory performance (Westlye et al. 2012), so that genetic status may mediate the relationship between MRI findings and cognition, too.

Besides these common MRI techniques, other approaches including diffusion kurtosis imaging, relaxometry, and magnetic transfer imaging may prove to be helpful in investigating WM integrity with high accuracy for whole brain mapping (Gouw et al. 2008; Struyfs et al. 2015). However, the number of studies using these approaches within the AD continuum is currently relatively small.

2.2 Utility in MCI

Image-based markers may be particularly interesting as they show a statistically significant prediction ability for those MCI subjects who will develop AD dementia. The prediction of conversion from MCI to AD dementia has been widely investigated (see Table 12.2), and there is agreement in imaging analysis being essential to this purpose. The most challenging task appears to be the distinction between normal controls (NC) and MCI subjects as the classification accuracy of most of the studies is lower compared to other groups of subjects (Leandrou et al. 2018).

Table 12.2 Selected quantitative MRI studies assessing the prediction of the conversion from MCI to AD (modified by Leandrou et al. 2018)

Full size table

Although hippocampal formation might be the most frequently used structure for the assessment of AD, the earlier involvement of the entorhinal cortex was proved by many studies and confirmed to be the relevant structure in the comparison of NC and MCI/AD (Juottonen et al. 1999; deToledo-Morrell et al. 2004; Killiany et al. 2002; Busatto et al. 2003; Tapiola et al. 2008; Gómez-Isla et al. 1997). Shape analysis appears to be a better technique compared to volume analysis, with similar results as voxel-based methods. While the entorhinal cortex is a better predictor compared to other structures, such as the hippocampus, the highest predictive accuracy (93.5% and 93%) was achieved when both were combined (deToledo-Morrell et al. 2004; Killiany et al. 2000). MTL atrophy also differentiates AD dementia from DLB and Parkinson’s disease with dementia (PDD), as AD patients show the greatest reduction in hippocampal volume (Delli Pizzi et al. 2016; Tam et al. 2005).

Other studies focused on the other MTL and limbic structures besides hippocampus and entorhinal cortex and how MTL atrophy discriminates those who will convert from MCI to AD dementia from non-converter MCI patients (Nesteruk et al. 2015). In the former, decreased inferior frontal gyrus volume was associated with verbal memory decline (Defrancesco et al. 2014), suggesting extratemporal involvement as a predictor of disease progression. In addition, atrophy in MCI is found in the primary olfactory cortex (Kilimann et al. 2014), infratentorial brain areas including the cerebellum and brainstem (Lee et al. 2015; Tabatabaei-Jafari et al. 2017), and some basal forebrain cholinergic system structures (Duarte et al. 2006; Kilimann et al. 2014; Vasavada et al. 2017).

Outside the GM, WM studies suggested that precuneus WM volume reduction was also associated with worsened memory in aMCI (Meyer et al. 2013). In addition, periventricular white matter hyperintensities (WMHs) are predictive of progression from MCI to AD dementia, with an increase of one point in WMH rating associated with a 59% increased risk of phenoconversion (ECW et al. 2008). Similarly, increased number of WMHs in the bilateral periventricular regions, as expression of a more severe vascular impairment, was related to worse recall performance in MCI (Fujishima et al. 2014).

2.3 Combined Use of MRI and Other Biomarkers

Nowadays, medical image analysis has become a computationally rich process that includes many intricate steps run on increasingly larger datasets with the use of many different tools and combined biomarkers. These biomarkers yield complementary information, as different modalities capture disease information from different perspectives and thus better reflect the neuropathological pattern than one single modality.

The combination of structural MRI, PET, and CSF biomarkers together with genetic data and neuropsychological status exam scores has been largely assessed. For example, the SPARE-AD score, summarizing brain atrophy patterns (Davatzikos et al. 2009; Fan et al. 2007) was combined with cognitive scores, APOE genotype, and CSF biomarkers (Da et al. 2014) to predict conversion from MCI to AD dementia. Similarly, Kohannim et al. (2010) found that the highest accuracy (82.2%) for AD classification was achieved when MRI hippocampal volume and ventricular summary, APOE genotype, and age were used together. Also, a benefit of combining MRI and FDG-PET in predicting conversion to AD dementia with respect to the single biomarker was reported (Walhovd et al. 2010) and was even higher when CSF was added (combinatorial accuracy of 92.1%) (Shaffer et al. 2013). However, other studies showed more modest results as adding CSF biomarkers to FDG-PET after MRI (i.e., CSF + MRI + FDG-PET) led to a 76.4% accuracy in distinguish MCI from healthy controls in a ADNI group, whereas the best biomarker taken alone (MRI) reached a 72% accuracy (Zhang et al. 2011). Moreover, just a 6% AUC value increase was demonstrated from the best single (t-Tau, AUC 0.77) to the best three-predictor combination model (t-Tau/HCV/CDR-sum of boxes, AUC = 0.83) in predicting conversion to AD dementia (Frölich et al. 2017). An overview of the possible approaches when combining biomarkers is shown in Fig. 12.2.

Concerning disease progression, morphometric measures derived from structural MRI can provide similar results with cellular or metabolic markers such as CSF, amyloid-Aβ, and FDG-PET (see (Schroeter et al. 2009) for a systematic and quantitative meta-analysis). Using images from the ADNI database with their corresponding segmentation masks (Heckemann et al. 2011), established regions of interest (ROIs) yielded an AUC of 0.79 in predicting progression to AD dementia in MCI patients (Martinez-Torteya et al. 2014). A similar performance was obtained with the combination of biomarkers derived from structural and functional imaging modalities (Jack et al. 2008a; Martínez-Torteya et al. 2015) as well as with the combination of MRI with genomic analysis. It was suggested that atrophy in the (trans-) entorhinal area/hippocampus and hypometabolism/hypoperfusion in the inferior parietal lobules could predict most reliably the progression from aMCI to AD dementia. However, according to a comprehensive meta-analysis (Yuan et al. 2009), FDG-PET was a better dementia predictor than morphological MRI.

Structural-based biomarkers are accurate predictors of outcome in symptomatic AD patients (Vemuri et al. 2009; Frisoni et al. 2010; Jack et al. 2009) and can be useful even in pre-symptomatic subjects (Pennanen et al. 2004; Killiany et al. 2002; Davatzikos et al. 2008) even more than metabolic markers at least according to some studies (Desikan et al. 2010; Vemuri et al. 2009; Frisoni et al. 2010).

Of course, imaging as well as all other biomarkers has value only if properly embedded in the clinical context (Devanand et al. 2007; Jack et al. 2004; Julkunen et al. 2009). To date, however, there are very few studies that combine clinical variables with volume, thickness, shape, intensity, and texture in multivariate assessment of the disease, which in turn may result in better classification and prediction accuracies (Cuingnet et al. 2011; Wolz et al. 2011).

3 Functional MRI, Arterial Spin Labeling (ASL), and Diffusion-Weighted Imaging

3.1 Blood-Oxygen-Level-Dependent (BOLD) Functional MRI (fMRI) Principles

BOLD enables the assessment of hemodynamic changes associated with regional brain activity during a task (e.g., a motor or a cognitive task). Hemoglobin (Hb) in erythrocytes delivers oxygen to neurons. When neurons are activated, there is an increased demand for oxygen, eliciting an increase of rCBF to the brain regions involved in neural activity. Hb is diamagnetic when oxygenated, while it is paramagnetic when deoxygenated. This difference in magnetic properties is associated with small differences in the magnetic resonance signal of blood, depending on the degree of oxygenation. Since blood oxygenation changes according to the neural activity, these differences can be used to explore brain activity. A detailed description of the neurophysiological basis of BOLD signal can be found in Logothetis (2002).

3.2 Utility in MCI

Performing fMRI studies in patients with cognitive impairment can be challenging. Even small head motions can compromise fMRI data quality and differences in task performance between patient and control groups, thus making the interpretation of data tricky or even misleading (Price and Friston 1999). Since the loss of memory is a key clinical feature of AD patients at all stages of the disease, anatomical regions and networks involved in memory have been explored by fMRI with appropriate tasks. Functional MRI has been utilized in normal subjects to investigate brain activity in memory processes in order to understand how neural activation differs, in memory tasks, for subsequently remembered or forgotten experiences. Successful retrieval of information is associated with fMRI evidence of activation in the MTL, prefrontal cortex, and ventral temporal cortex (Wagner et al. 1998; Kirchhoff et al. 2000; Brewer et al. 1998). Moreover, fMRI studies have demonstrated a brain network that “deactivates” during successful memory formation (Daselaar et al. 2004). These data evoke a theoretical model of memory formation that requires activation of the hippocampal nodes and concomitant deactivation of the retrosplenial-parietal nodes.

Functional MRI studies in MCI report different, and apparently conflicting, results when evaluating task-related BOLD signal changes. Many previous task-based fMRI studies showed a greater activity in MCI patients with respect to healthy controls in frontal and parietal regions across memory encoding and retrieval, working memory, executive function, and perception tasks (Bokde et al. 2010; Kaufmann et al. 2008; Poettrich et al. 2009). However, in a meta-analysis healthy elderly showed greater activity in MTL and frontal pole both in encoding and retrieval processing while an increased activation in ventral lateral prefrontal cortex, superior temporal gyrus was detected in AD patients (Schwindt and Black 2009). Browndyke et al. (2013) demonstrated a decreased activation in MTL and increased activation in prefrontal gyrus in both MCI and AD dementia patients with respect to healthy controls during memory encoding. Interestingly, other studies have found greater MTL activation in MCI with respect to controls, for instance, in an associative face-name encoding paradigm (Dickerson et al. 2005) or in an associative encoding of novel picture-word pair task (Hämäläinen et al. 2007). It seems that heterogeneity in patients’ selection and methodological aspects cannot completely explain this variability in fMRI studies results. In fact, many authors hypothesize that there is a phase of increased MTL activation early in the course of MCI, most likely representing a compensatory response to AD pathology. Beyond this phase, as the disease progresses, there is a decrease in functional activation. This “compensatory” hypothesis is supported by data derived from studies in cognitively intact APOE-epsilon4 allele carriers (Bookheimer et al. 2000). Such MTL hyperactivation could be useful to predict progression from MCI to AD dementia (Dickerson et al. 2005); however, a correct interpretation of fMRI hyperactivation with respect to particular brain regions and behavioral conditions is not always straightforward and might indeed pose interpretation problems. Considering a theoretical model with an “inverse U-shaped curve” of fMRI activation along the course of the disease, hyperactivation can be detected in the setting of minimal clinical impairment (and relatively little MTL atrophy) in the upgoing phase along the curve, while the same level of hyperactivation in the setting of more prominent clinical and memory impairment and MTL atrophy would be consistent with the downgoing phase of the curve. Thus, the same degree of fMRI hyperactivation may be associated with different biological stages of disease, a result that would make it difficult to use fMRI for clinical purposes at the MCI stage.

Recently, 39 task-based fMRI studies (697 MCI patients and 628 healthy controls) were included in MCI-related meta-analysis, while 36 task-based fMRI studies (421 AD patients and 512 healthy controls) were included in AD-related meta-analysis (Li et al. 2015). MCI and AD dementia patients showed abnormal regional brain activation but also alterations within large-scale brain networks. Relative to healthy controls, MCI patients showed hypoactivation in the default mode network (DMN) and in the frontoparietal and visual networks. AD-related hypoactivation was mainly located in visual, default, and ventral attention networks relative to healthy controls. Both MCI-related and AD dementia-related hyperactivation were identified in the frontoparietal, ventral attention, default, and motor networks with respect to healthy controls. Thus, it may be suggested that MCI and AD dementia share similar compensatory mechanisms in large-scale networks actuated in relation to different cognitive tasks.

Brain activation on fMRI may also be affected by cognitive training. Mnemonic strategies, frequently used as a part of cognitive rehabilitation programs, are effective in some patient populations (Cicerone et al. 2011) and result in increased prefrontal (Kondo et al. 2005; Miotto et al. 2006) and hippocampal (Nyberg et al. 2003) activity in healthy participants. Some authors have reported increased prefrontal activity accompanying behavioral improvement after cognitive training in MCI patients (Hampstead et al. 2011; Belleville et al. 2011) and a facilitation of hippocampal function (Hampstead et al. 2012). In conclusion, with increasing fMRI studies in AD, it has been demonstrated that some cortical regions are activated in a large variety of tasks. It may be suggested that AD is characterized by multiple and large-scale dysfunctional neuronal networks and not specifically by alterations in single brain regions.

Another field of research is represented by resting-state fMRI (rs-fMRI) and the study of “default mode” activity in people with MCI and AD. This fMRI modality has some advantages, particularly in the study of individuals with more severe cognitive impairment who may have difficulties performing a given task. Resting-state functional connectivity explores temporal correlations of BOLD signal between different brain regions and/or voxels. Studies examining rs-fMRI data have found low-frequency fluctuations of the measured cerebral hemodynamics (Biswal et al. 1995). These signal variations, temporally correlated across the brain, correspond to functional resting-state networks that together characterize the neuronal baseline activity of the human brain in the absence of stimulated neuronal activity. The coherent resting fluctuations that have been identified include functionally relevant networks involved in motor function, visual processing, executive functioning, auditory processing, memory, and the DMN. This network is active during resting-state but has a decreased activity during the performance of cognitive tasks, which indicates that the DMN is fundamental for modulating cognitive processing. MCI patients are characterized by a disruption of functional connectivity within the DMN (Greicius et al. 2004; He et al. 2007; Wang and Su 2006; Han et al. 2011; Koch et al. 2015; Zhang et al. 2012b) that included the ventral medial prefrontal cortex, dorsal medial prefrontal cortex, posterior cingulate cortex (PCC), adjacent precuneus, lateral parietal cortex, hippocampus, and thalamus (Lin et al. 2018). Subsequent studies showed an increase in functional activity in the frontal cortex, besides a decrease in functional activity in the DMN regions (Jin et al. 2012). Hyperactivation in brain regions belonging to memory and cognitive circuits may possibly represent an attempted compensatory activity. Besides the methodological differences between the different studies, functional connectivity in the hippocampus and PCC has been demonstrated to be consistent with poor performance of neuropsychological tests in MCI, suggesting the potential role of functional connectivity as a predictor of cognitive decline (Han et al. 2011; Binnewijzend et al. 2012; Gardini et al. 2015). Considering that the brain is an intrinsically variable system, recently standard deviation of BOLD fluctuations (SD_BOLD) (instead of the mean value) has been proposed as a biomarker of AD, also to investigate the role of cerebrovascular status in determining BOLD variability (Scarapicchia et al. 2018), in the hypothesis that fluctuations on functional activity simply reflect vascular processes unrelated to neuronal function (Obrig et al. 2000; Wise et al. 2004).

Recently, more complex models, such as regional homogeneity (ReHo) (Zhang et al. 2012b; Lin et al. 2018; Zang et al. 2004; Bai et al. 2008), amplitude of low-frequency fluctuation (ALFF) (He et al. 2007; Pan et al. 2017), and graph theory have been used to explore the pathological process in MCI patients (Zhang et al. 2017) which is interesting in research but still far from being utilized in clinical practice.

The integration of task-free fMRI techniques able to identify the different networks involved in MCI and AD patients with tau PET and amyloid PET may be helpful to detect the interplay between tau and Aβ temporal and anatomical pattern of deposition across the brain with brain networks modifications and failure. Jones et al. (2017) demonstrated that younger age of disease onset was associated with tau deposition in nonclassical brain areas (“non-Braak-like” pattern), suggesting an association with atypical clinical phenotypes. Furthermore, the authors demonstrated that amyloid is a partial mediator of the relationship between functional network failure and tau deposition in functionally connected brain regions. While Aβ deposition reaches a plateau, tau deposition, which involves more specific functional networks related to AD, continues to increase over the time and correlates with clinical progression.

3.3 Arterial Spin Labeling (ASL)

Arterial spin labeling (ASL) is a noninvasive MRI technique able to reveal typical brain perfusion abnormalities in several neurological conditions without the need of injecting any contrast agent. In MRI-ASL inflowing arterial blood water is magnetically tagged proximally to the region of interest using a radiofrequency inversion pulse. Then, quantitative perfusion maps are obtained by subtracting the brain images without and with labeling of blood (Riederer et al. 2018).

ASL-MRI technique is known for human use for over a decade; in the last years, improvements in SNR and reliability have been achieved, and now several variants of ASL-MRI techniques are widely applied to neurodegenerative populations, including continuous ASL (CASL), pulsed ASL (pASL) (Xu et al. 2009; Ciarochi et al. 2016), and pseudo-continuous ASL (pCASL) (Ciarochi et al. 2016; Ferreira et al. 2011). The ISMRM perfusion study group and the European consortium for ASL in dementia recently provided a consensus including recommendations to optimize MRI-ASL images acquisition and analysis (Alsop et al. 2015).

Given the physiological coupling between metabolism and perfusion, in several studies about AD, both FDG-PET and MRI-ASL were acquired and compared to evaluate if the information provided by the different techniques is redundant or complementary (Chen et al. 2011a; Musiek et al. 2012; Verfaillie et al. 2015). ASL-MRI demonstrated that resting rCBF is able to predict conversion to MCI (Beason-Held et al. 2013) in adult healthy individuals; compared to individuals with preserved cognitive functions, adults that developed MCI showed hypoperfusion in parietal, temporal, and thalamic regions, suggesting that perfusion abnormalities take place years before the onset of cognitive symptoms. In accordance with FDG-PET literature, ASL-MRI performed in MCI patients (Johnson et al. 2005) reported decreased rCBF in posterior cingulate/precuneus and parietal regions, with a lesser extent than in AD dementia. A few longitudinal ASL-MRI studies have been performed in MCI patients (Chao et al. 2010); patients who converted to AD dementia showed greater hypoperfusion in the precuneus, middle cingulum, infero-parietal, and middle frontal cortices. When combined with measures of hippocampal volume at baseline, rCBF values provided additional predictive power.

In patients with AD dementia, regional CBF (rCBF) decrease was more prominent in the precuneus, posterior cingulate, and superior parietal cortex (Alsop et al. 2000); additional areas of hypoperfusion have been described in the lateral frontal lobe, orbitofrontal cortex, and temporal lobe including the parahippocampal gyrus and hippocampus (Wierenga et al. 2012). Interestingly, increased rCBF has been described in frontal regions and hypothesized to reflect compensatory or pathological elevation of neural activity, inflammation, or elevated production of vasodilators (Alsop et al. 2008). Also, the MTL has been reported to be relatively hyperperfused in early-stage AD patients with respect to controls (Chao et al. 2010). This feature is in contrast to a number of FDG-PET studies that have reported MTL hypometabolism (Chen et al. 2011a; Takahashi et al. 2014; Rodriguez et al. 2000). Time interval between MRI-ASL and FDG-PET scans has been hypothesized to be the cause of these concerns. Thus, in a very recent study (Riederer et al. 2018), integrated pulsed MRI-ASL and FDG-PET scan revealed similar findings in patients with AD dementia, while in MCI patients, FDG-PET was more sensitive with respect to MRI-ASL in detecting quantitative hypometabolism in the precuneus. Future investigations based on MRI-ASL and on the association of MRI-ASL and FDG-PET in neurodegenerative disorders are necessary to evaluate the real role of this technique for early diagnosis and disease monitoring. This would be particularly meaningful using simultaneous PET-MRI acquisitions with dedicated PET-MRI scanners and might lead to clinical validation of ASL techniques in dementia diagnosis and prognosis.

3.4 Diffusion-Weighted Imaging (DWI) Principles

Diffusion-weighted imaging and related techniques, such as diffusion tensor imaging (DTI), are quantitative MRI modalities that measure water diffusion properties noninvasively and that have evolved as a reliable technology to probe central nervous system microstructure. DTI is highly sensitive to subtle structural changes in brain tissue, given the diffusion anisotropy characterizing WM. Thus, in normal WM, diffusion is not identical in all directions, but, for a given diffusion time, displacement of water molecules is on average greater along the length of axons than across the axons (Basser et al. 1994; Moseley et al. 1990). Several DTI-derived metrics can be obtained, including fractional anisotropy (FA) (Beaulieu 2002), mean diffusivity (MD), and parallel (i.e., axial diffusivity, DA) and perpendicular diffusivity (i.e., radial diffusivity, RD). In general, a decrease in anisotropy is considered a marker of loss of white matter integrity. Anisotropy can be reduced due to a decrease in parallel diffusivity and an increase in perpendicular diffusivity or some combination of these parameters. Even though inferring the histological contributors of anisotropy changes is not straightforward, experimental studies have shed some light on the biological basis of the different DTI-derived parameters. For instance, decreases in DA have been correlated with axonal loss, whereas increases in RD would indicate loss of myelin (Song et al. 2003). The diffusion measurements obtained in a DTI acquisition can be analyzed using different approaches. Among them, region-of-interest (ROI) analysis and voxel-based methods have been used in the study of MCI. Moreover, DTI can be used to isolate white matter pathways in vivo and thus enables diffusion measurements along a given anatomical structure. As an example, DTI can be used to obtain measurements within the voxels estimated to belong to a white matter bundle of interest.

Although DTI longitudinal reproducibility has been shown to be acceptable with different equipment used in healthy subjects (Jovicich et al. 2014), the use of different methodologies and equipment may account for the heterogeneous findings reported in different studies. Nonetheless, even studies that use the same methods of DTI data analysis may be difficult to compare. In fact, patients’ characteristics may vary among studies; DTI acquisition parameters are often very different (the technical details of DTI are beyond the scope of this chapter, but many acquisition parameters such as “b” values and acquisition matrix can be dissimilar among studies). Also, when comparing “significant” findings among different studies, it must be noted that different statistical approaches can be utilized, and when comparing results from multiple ROIs, for example, more or less “liberal” statistical thresholds will lead to different results (Sexton et al. 2011). The single tensor DTI model assumes a single tissue compartment per voxel, and this represents a limitation, thus leading to DTI metrics in voxels consisting of a mixture of WM and freely moving extracellular water molecules (Alexander et al. 2001; O’Donnell and Pasternak 2015). This may be particularly concerning in patients with neurodegenerative disease, since, along with brain atrophy, increase in water within a voxel may significantly influence diffusion derived metrics. Thus, multi-compartmental models were initially employed to detect water contamination within voxels but, as described below, also enable better characterization of tissue microstructure.

3.5 Utility in MCI

Using DTI, researchers have contributed to amplify the interest on WM pathology in MCI (and AD dementia) and added information to the debate on the relevance of WM pathology and on its pathogenesis. The most important question is whether WM pathological changes are related to GM pathology or not. WM pathology may occur as a consequence of GM pathology that first involves the hippocampus and the entorhinal cortex and then progresses to temporal and parietal association cortex; thus, WM microstructural damage would be the result of secondary degeneration. Other researchers hypothesize that WM and GM pathologies would be independent and would follow the myelogenesis pattern in a reverse order (Bartzokis 2004). Vascular changes may also contribute to WM pathology (Brun and Englund 1986).

DTI studies can be useful to better understand the relationship between GM and WM changes. For instance, an interesting study in AD patients found a significant regional correlation between FA within the corpus callosum and GM thickness in the lobes these fibers connect with (Sydykova et al. 2007); the authors concluded that decline of FA in the corpus callosum could be related to neuronal degeneration in corresponding cortical areas. Nevertheless, in the published DTI studies, there is no overall consensus, with some studies supporting secondary degeneration and others primary myelin pathology.

DTI studies have extensively showed, in vivo, the presence of WM changes in MCI patients with respect to control subjects. At first, studies using regional approaches reported changes in MD and anisotropy in the MTL, especially within the hippocampal formation (Wang and Su 2006; Kantarci et al. 2001), and higher baseline hippocampal MD has been reported useful in identifying patients with MCI who will progress to AD (Fellgiebel et al. 2006). WM disruption occurring in the preclinical stage of AD has been confirmed in more recent studies (Adluru et al. 2014; Fischer et al. 2015; Kantarci et al. 2014; Prescott et al. 2014), initially involving MTL association tracts and then spreading to the temporal and parietal white matter during clinical disease progression (Demirhan et al. 2015; Konukoglu et al. 2016). Other anatomical locations have then been assessed; using multiple ROIs, Zhang et al. (2007) identified the posterior cingulate, at the apex of the posterior curve of the tract, as the most suitable region in discerning between healthy controls and MCI or AD dementia. In that study, MTL volume was only 63% accurate for discriminating between healthy controls and MCI, but accuracy increased to 78% with the information derived from left posterior cingulate FA. An example of DTI findings in a patient with aMCI is reported in Fig. 12.3.

Voxel-based approaches can be utilized to overcome the limitation represented by the analysis of an individual ROI instead of the whole brain. Using the technique of tract-based spatial statistics, O’Dwyer et al. (2011) studied 19 patients with naMCI and 14 with aMCI subjects and found significantly higher DA in naMCI subjects compared to healthy controls in the right posterior cingulum/precuneus. They also found significantly higher DA in aMCI subjects compared to healthy controls in the left prefrontal cortex, particularly in the forceps minor and uncinate fasciculus. In a study of 96 aMCI and 69 naMCI subjects, Zhuang et al. (2010) found that the best discrimination between aMCI and controls was achieved by combining FA measures of the splenium of corpus callosum and crus of fornix, with accuracy of 74.8%.

The single tensor DTI model assumes a single tissue compartment per voxel, and this represents a limitation, thus leading to DTI metrics in voxels consisting of a mixture of WM and freely moving extracellular water molecules (Alexander et al. 2001; O’Donnell and Pasternak 2015). To overcome this concern, free water elimination (FWE) methods including an explicit compartment modeling free water have been proposed (Metzler-Baddeley et al. 2012; Baron and Beaulieu 2015), with interesting applications in longitudinal studies. Indeed, longitudinal changes in white matter microstructure may be related to brain slice positioning across MRI sessions, leading to different CSF-contamination-based partial volume effects (Metzler-Baddeley et al. 2012). Furthermore, the extracellular volume may be influenced by transient changes, such as dehydration, temperature, and stress, which may change between scans. In a recent study, longitudinal test-retest reproducibility of DTI metrics commonly used in clinical studies derived from the single tensor DTI model has been compared to those provided by a bi-tensor FWE diffusion model (Albi et al. 2017). In that study the authors demonstrated that FWE is characterized by a reduced reproducibility error in the majority of brain regions tested and by a better sensitivity in identifying more subtle changes, thus representing a promising tool for clinical applications.

Besides bi-tensor models, multi-shell diffusion MRI techniques have been developed in the last years, allowing an even more advanced analysis of the diffusion signal. The association between WM microstructure and performance on working memory tasks in healthy adults has been analyzed (Chung et al. 2018). Specific WM tract integrity (WMTI) metrics provided by multi-shell diffusion MRI analysis and diffusion tensor/kurtosis imaging (DTI/DKI) metrics have been used to describe microstructural characteristics in both the WM intra- and extra-axonal environments: axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial, and radial diffusivities. These parameters allow a better biophysical interpretation of WM changes with respect to “conventional” DTI analysis. In that study, a positive correlation between AWF and letter-number sequencing (LNS) was demonstrated, suggesting that higher AWF may correspond to a greater axonal volume and myelination, leading to faster information processing.

Neurite orientation dispersion and density imaging (NODDI) (Zhang et al. 2012c) represents another possible application of multi-shell diffusion MRI techniques, able to provide tissue-specific microstructural information from multiple compartments within a voxel. The basic assumption of NODDI model comes down to the fact that water molecules in neuronal tissue may be placed in three different compartments: (a) free water, representing CSF; (b) restricted water, representing neurites; and (c) hindered water, representing diffusion within glial cells, neuronal cell bodies, and the extracellular environment. Through NODDI processing, different measures may be obtained, i.e., the neurite density index (NDI) (higher values reflect increased neurite density) and the orientation dispersion index (ODI), that give information about the degree of neurite dispersion. Furthermore, thanks to the capacity to estimate the free water fraction, the risk of partial volume effects due to the CSF is notably reduced with respect to the previous DTI models (Zhang et al. 2012b). In efforts to correlate diffusion derived metrics with pathology, differences in cortical NODDI measurements have been described in transgenic murine models of AD tauopathy (Colgan et al. 2016) finding a lower cortical ODI compared to wild type mice but increased cortical NDI that, interestingly, was associated with a greater tau immunoreactivity. This feature is discordant with a more recent study (Parker et al. 2018) in which a consistent decrease in NDI in early-onset AD (EOAD) patients compared to healthy controls was demonstrated in entorhinal, middle temporal, inferior temporal, fusiform, precuneus, and precentral areas and reductions in ODI in inferior temporal, middle temporal, fusiform, and precuneus regions. Interestingly, these findings persisted in the majority of regions after performing adjustment for cortical thickness, and, concerning NDI, abnormalities were detected also in the precentral gyrus, a region typically known to be spared of significant atrophy in AD. Furthermore, cortical NDI correlated with the Mini-Mental State Examination (MMSE) score.

3.6 Combined Use of DTI and Other Biomarkers

A few studies have tried to assess the relationship between cerebrovascular fluid biomarkers (CSF) and brain tissue microstructure using DTI indices (Bendlin et al. 2012; Gold et al. 2014). In a small group of adults with parental history of AD, CSF total tau and total tau/aβ_1-42 ratio correlated with mean, axial, and radial diffusivity in WM regions next to GM areas affected in early AD, while there was no correlation between CSF biomarkers and GM volume (Bendlin et al. 2012). Similar results were described in another study that evaluated a group of adults at high risk of AD, as defined by parental history and APOE ε4 status and a group of subjects with preclinical AD pathology, assessed by CSF markers. By using DTI the authors identified an association between changes in MD with higher CSF hyperphosphorylated tau/aβ_1-42 ratio, particularly in the temporal lobe WM, but not in hippocampal GM (Hoy et al. 2017). WM-DTI abnormalities have been also associated with imaging biomarkers suggestive of gray matter neurodegeneration such as cortical and hippocampal atrophy (Ouyang et al. 2015) and hypometabolism on FDG-PET (Kantarci et al. 2014). Furthermore, WM degenerative changes provided by DTI analysis correlate with cognitive function and disease severity. Finally, Kantarci et al. (2017) correlated MD and FA values of the limbic projections and white matter regions that showed neurofibrillary tangles on neuropathological examination. They found that the higher cortical neurofibrillary tangle burden was associated with a higher MD and lower FA in the correspondent tracts and regions.

In conclusion, DTI studies indicate a pathological involvement of WM in MCI. The nature of these pathological changes, primary or secondary, is still a matter of debate. Thus, integrative studies including both DTI analysis in vivo and neuropathology postmortem may be of paramount importance to increase our knowledge about the biological correlates of microstructural parameters of tissue damage in MCI and AD dementia patients.

4 Perfusion SPECT

4.1 Principles

The most common radiopharmaceutical tracers used for brain perfusion SPECT are ^99mTc hexamethylpropylene amine oxime (HMPAO) and ethylcysteinate dimer (ECD). ¹²³I-Iodoamphetamine (IMP) is also used, mainly in Japan. Those techniques measure the nonquantitative distribution of a perfusion tracer within the brain. Thus, even if the term “rCBF” is often used to report SPECT findings, the most appropriate term would be “brain perfusion,” and “rCBF” should be reserved to “quantitative” measures, as obtained with H₂¹⁵O PET techniques.

General-purpose gamma cameras equipped with two or three head detectors and low-energy, high-resolution collimators are usually used, and such equipment is widely available. However, brain-dedicated cameras and fan-beam collimators, both yielding a higher spatial resolution, are less available. The newer SPECT-computed tomography (SPECT-CT) equipment has substantially increased image quality by improving attenuation correction computation. Three-dimensional imaging with fair spatial resolution (i.e., 6–8 mm) is allowed by reconstructing bidimensional projections. Moreover, to obtain reliable imaging, both acquisition and reconstruction protocols are crucial, and the procedural guidelines of the American Society (Juni et al. 2009) and of the European Association (Kapucu et al. 2009) of Nuclear Medicine should be strictly followed.

4.2 Utility in MCI

Patients with aMCI show hypoperfusion patterns that closely overlap FDG-PET patterns, even if the two modalities actually measure different parameters. However, literature studies have shown that both FDG and perfusion SPECT tracers mainly reflect the function of astrocytes that are closely connected to synaptic neurotransmission (Magistretti et al. 1999). Thus, the obtained imaging is similar, even if some differences can be found, mainly between ^99mTc-ECD and ^99mTc-HMPAO (Koulibaly et al. 2003), whereas ^99mTc-ECD images are more similar to FDG-PET ones.

The typical hypoperfusion pattern in aMCI patients includes the association portion of the parietal lobe, the posterolateral part of the temporal lobe, and the precuneus (Borroni et al. 2006; Encinas et al. 2003; Hirao et al. 2005; Høgh et al. 2004) (Fig. 12.4). Less frequently, hypoperfusion in the posterior cingulate has been reported in MCI patients, mainly in studies using ^99mTc-HMPAO (Huang et al. 2003; Johnson et al. 2007) or ¹²³I-iodoamphetamine (Ishiwata et al. 2006). On the other hand, studies using ^99mTc-ECD often failed in demonstrating posterior cingulate hypoperfusion (Borroni et al. 2006; Encinas et al. 2003; Nobili et al. 2008a). Then, when AD dementia develops, hypoperfusion becomes clearer in lateral frontal association cortex as well.

SPECT images at the aMCI stages of AD are often asymmetric reflecting “lateralization” of neuropsychological impairment, as language and visuospatial disturbances predominate in left and right hemisphere affection, respectively. Age at onset is a source of strong variability in AD. Indeed, in EOAD (i.e., <65 years) neocortical (i.e., posterior temporal-parietal) impairment predominates, whereas in late-onset AD (LOAD) dysfunction is more evident in MTL structures. Again, neuroimaging findings usually reflect neuropsychological profiles; thus, EOAD cases show a neocortical presentation with various combinations of symptoms within the triad aphasia-apraxia-agnosia and a relatively spared memory, while the opposite happens in LOAD cases. Brain perfusion pattern may be influenced by genetic traits. Thus, aMCI patients carrying at least one ε4 allele of the APOE gene have more evident hypoperfusion than severity-matched aMCI patients without ε4 alleles.

In patients with MCI, baseline SPECT hippocampal perfusion was able to predict conversion to AD dementia after 2 years of follow-up. Moreover, MCI patients with cognitive worsening over time showed hypoperfusion in inferior parietal lobule in comparison with stable MCI patients. In those patients, either baseline hippocampal or parietal perfusion was significantly correlated with verbal delayed recall score at follow-up. Moreover, receiver operating characteristic (ROC) curves for hippocampal perfusion showed 0.81 sensitivity with 0.86 specificity in differentiating converters from non-converters. Thus, baseline SPECT can support outcome prediction in subjects with MCI (Nobili et al. 2009a). In order to study functional compensation in aMCI patients, MRI imaging co-registered with perfusion SPECT found relatively preserved perfusion in posterior cingulate, in the head of the hippocampus, in the amygdala, and in the insula bilaterally. Functional depression was instead disclosed in bilateral parahippocampal gyri. Thus, assessing functional compensation of neuronal loss as a phenomenon of brain reactivity could be helpful in order to understand those mechanisms counteracting the pathological changes of AD (Caroli et al. 2010). However, aMCI is not only a prerogative of patients later developing AD dementia, as it has been demonstrated in a part of patients with Parkinson’s disease (PD) (Janvin et al. 2006). Even in PD patients with aMCI, perfusion SPECT is able to show mainly parieto-occipital hypoperfusion compared to aMCI patients without PD who instead show predominant MTL hypoperfusion (Nobili et al. 2009b). These findings may help identifying those PD patients with an amnestic syndrome who are therefore at higher risk of developing dementia in the short-to-medium term.

SPECT literature is less exhaustive in naMCI patients. One study disclosed statistically significant, reduced perfusion values in bilateral temporal cortex in naMCI patients compared to controls, but not significant with respect to aMCI or subjects with subjective memory complaints (Nobili et al. 2008b). Furthermore, naMCI patients showed hypoperfusion in right frontal cortex compared to all the other groups, significantly with respect to subjects with subjective memory complaints. Notably, naMCI patients had higher prevalence of arterial hypertension, depression, and white matter hyperintensities (WMHs) in MRI scan suggesting vascular damage and thus a non-neurodegenerative etiology of the cognitive impairment. In fact, executive dysfunction is thought to be driven especially by the activity of the dorsolateral prefrontal cortex and has been associated with WMHs in MCI patients (Reed et al. 2004). Also depression is associated with frontal hypoperfusion, with or without WHMs (Oda et al. 2003). Moreover, reduced rCBF in frontal and temporal areas is associated with hypertension, which frequently underpins chronic hypoperfusion and WMH (Rodriguez et al. 1987).

Though the lower spatial resolution makes subtle brain abnormalities less evident than with FDG-PET, perfusion SPECT could be more informative in MCI due to vascular disease, where glucose metabolism could be spared. Pros and cons of PET and SPECT have been well debated and analyzed in specialized reviews (Ishii and Minoshima 2005; Pupi and Nobili 2005), where also cost of PET has been evaluated; PET is indeed more expensive if FDG is bought from an external supplier, but price may be more affordable if it is produced in-house. Talking about brain blood perfusion, it should be reminded here that nowadays there is an increasing evidence that also early (postinjection) data acquisition of [¹⁸F]-labeled amyloid PET tracers could be informative on brain perfusion distribution that is correlated with brain metabolic levels (Daerr et al. 2017).

4.3 Combined Use of SPECT and Other Biomarkers

Few studies have compared perfusion SPECT and MRI techniques (Takahashi et al. 2014; El Fakhri et al. 2003); notably, the diagnostic performance of ASL-MRI and SPECT in distinguishing AD from non-AD was almost equivalent, even though ASL-MRI was more influenced by hemodynamic factors (Takahashi et al. 2014). Furthermore, Habert et al. (2010) conducted a correlation study among brain perfusion SPECT and CSF biomarkers among patients with MCI, mild AD dementia, and a pooled population of control subjects. SPECT level of perfusion did not correlate with amyloidosis biomarker, such Aβ_1-42 levels. Instead, a significant correlation was found between brain perfusion in the left parietal cortex and either t-Tau or p-Tau concentrations. Thus, perfusion SPECT could be used as a marker of neurodegeneration, in agreement with Tau CSF level.

5 ¹⁸F-FDG-PET

5.1 Principles

¹⁸F-FDG-PET (or simply FDG-PET from now on) is able to estimate the local cerebral metabolic rate of glucose consumption (CMRgl), thus allowing in vivo evaluation of the distribution of neuronal death and synapse dysfunction (Herholz 2003). Indeed, glucose is phosphorylated by a hexokinase inside the neuron-astrocyte functional unit, and this is the first pivotal step of that metabolic pathway. At the synaptic terminals, energy is actively produced by the tricarboxylic acid pathway, using oxygen and leading to high ATP availability (aerobic glycolysis). On the other hand, astrocytes mainly utilize anaerobic glycolysis, which is faster but provides less energy. Therefore, glucose metabolism is closely related to both resting and active neuronal function (Magistretti 2000). FDG-PET is usually performed in a resting state and glucose uptake distribution is mainly driven by basal neuronal activity, representing general neuronal integrity (Herholz 2003). Thus, reduced glucose uptake basically reflects either reduced synaptic metabolic activity or a reduction in number of synapses. The specific molecular mechanisms of neuronal activity that are associated with energy metabolism have not been completely identified. However, the glucose metabolism measured by PET seems to be associated with glutamate-driven astrocytic glucose uptake (Magistretti et al. 1999; Mosconi 2005).

Radiolabeled glucose brain uptake takes about 20 min, thus FDG-PET has a very low temporal resolution. Therefore, any circumstance that alters “psychosensory resting” may significantly affect the results of the scan. The most relevant stimuli that must be avoided are speech and sensorial stimulation; however, also the vigilance state and anxiety should always be taken into account. Indeed, a significant inverse correlation between vigilance measures and FDG metabolism in bilateral frontal and temporal regions, bilateral cingulate gyrus, and right thalamus has been shown (Guenther et al. 2011).

5.2 Utility in MCI

AD and other types of dementia have been extensively studied with FDG-PET in the last 30 years (Mosconi 2005; Salmon et al. 2009). The research efforts moved then to pre-dementia stages and to MCI, mostly with the aim of identifying those patients suffering from AD in prodromal phase (Nobili et al. 2008a; Anchisi et al. 2005). Indeed, FDG-PET may provide useful information in this scenario, by showing (or not showing) specific disease patterns. A typical single or multi-domain aMCI patient with a clinical suspicion of AD has a high pretest probability to show typical brain metabolic abnormalities; thus the positive predictive value of FDG-PET is higher than 90%. Indeed, individual cases may show hypometabolism in posterior parieto-temporal cortex, lateral occipital cortex, precuneus, and PCC (Nobili et al. 2008a; Mosconi 2005; Morbelli et al. 2010). Among these, PCC hypometabolism is the most common finding in early AD patients (Minoshima et al. 1997); the reduced PCC activity found in prodromal AD patients may reflect decreased connectivity especially with entorhinal cortex and hippocampus, which are among the first regions affected by AD pathology (Meguro et al. 1999). The concept of metabolic disconnection of medial temporal cortex in early AD dementia was recently confirmed by the finding of lacking functional/metabolic connectivity between hippocampus and PC in prodromal AD patients, even without specific hippocampal hypometabolism (Morbelli et al. 2012). On the other hand, hippocampal hypometabolism is often not reported in FDG-PET studies, even if early hippocampal brain damage has been suggested to be related with hypofunction and likely reduced brain glucose metabolism. This is probably due to both limited spatial resolution of the first generations PET equipment and, particularly, to the use of the Statistical Parametric Mapping (SPM) analysis method (Mosconi 2005). To overcome the abovementioned issues in the evaluation of hippocampal hypometabolism, a different reorientation in SPECT clinical setting, the so-called Ohnishi transaxial plane (i.e., about 30° with the nose upward with respect to the bicommissural plane (Ohnishi et al. 1995)) has been successfully proposed for analyzing transaxial slices. In fact, the hippocampus is not parallel to the bicommissural line; thus the bicommissural plane is inadequate for hippocampus segmentation. This approach is appropriate for FDG-PET as well.

A very frequent FDG-PET finding in MCI patients is the strong asymmetric involvement, regardless of the cortical regions affected, and it is usually correlated with clinical symptoms and presentation. As already discussed for SPECT imaging, when left hemisphere hypometabolism is predominant, language impairment is more severe, whereas hypometabolism is predominantly right, visuospatial impairment is clearer. Therefore, visual analysis of scans strongly relies on evaluation of asymmetric abnormalities. Finally, the AD-related metabolic pattern, as identified by spatial covariance analysis, has been found in MCI patients, especially those converting to AD dementia (Meles et al. 2017).

Recently, the European Association of Nuclear Medicine (EANM) and the European Academy of Neurology (EAN) constituted a task force for the consensual recommendations of clinical use of FDG-PET in dementing neurodegenerative disorders (Nobili et al. 2018). In particular, the clinical utility of FDG-PET in MCI has been explored (Arbizu et al. 2018). The incremental value of FDG-PET, as added to the clinical-neuropsychological examination, to ascertain the etiology of MCI has been investigated. Harmonized population, intervention, comparison, and outcome (PICO) questions were used to this end. The first PICO question was whether FDG-PET should be performed, as adding diagnostic value as compared to standard clinical/neuropsychological assessment alone, to detect prodromal AD, and a fair relative availability of evidence was achieved (Arbizu et al. 2018). This was because it was acknowledged that both the typical PCC and posterior temporoparietal hypometabolism in the majority of studies of MCI due to AD (Fig. 12.5) and a normal FDG-PET scan reasonably exclude neurodegeneration due to AD. Twenty-one papers were examined, showing a large range of sensitivity (38–98%), specificity (41–97%), and accuracy (58–100%) values for the identification of prodromal AD patients (Arbizu et al. 2018).

Most FDG-PET studies have focused on the investigation of clinical and etiological heterogeneity of MCI, mainly in the amnestic cognitive domain. Few FDG-PET studies have been conducted in naMCI (Seo et al. 2009; Raczka et al. 2010; Boeve 2012). Dementia with Lewy bodies (DLB) is the second most common cause of dementia after AD (Vieira et al. 2013) and is characterized by cognitive decline associated with a combination of attention fluctuations, visual hallucinations, rapid eye movement sleep behavior disorder (RBD), and parkinsonism (McKeith et al. 2017). However, little effort has been made for the identification of DLB patients in the pre-dementia stage. Three prototypical forms of prodromal DLB have been proposed, namely, an MCI variant, associated with early visuo-perceptual and attentional deficits, a delirium onset DLB with provoked or spontaneous delirium as the presenting features, and a psychiatric onset DLB with its primary presentation as a late-onset affective disorder or psychosis (McKeith et al. 2016). Indeed, DLB is a heterogeneous disease, with clinical core features being associated with more prominent hypometabolism in specific regions (Morbelli et al. 2019). As discussed, MCI syndrome may be associated with Lewy body pathology, regardless of the coexistence of parkinsonism, and the cognitive impairment is often accompanied or even preceded by RBD. Those patients can have hypometabolism in the occipital cortex, other than the known decreased basal ganglia uptake on dopamine transporter (DAT) imaging with either SPECT or PET tracers (Boeve 2012). Indeed, idiopathic RBD patients with cognitive impairment are likely to develop DLB over time (Postuma et al. 2019). A longitudinal study evaluating 30 either aMCI or naMCI patients showed that those patients converting to DLB at follow-up exhibited hypometabolism in the posterior and anterior cingulate gyrus and in the parietal lobe at baseline (Clerici et al. 2009) that, along with the presence of the posterior cingulate island sign (i.e., relatively preserved metabolism in the posterior cingulate area), closely overlap with the typical DLB pattern (Fig. 12.5). Therefore, the clinical use of FDG-PET to support the diagnosis of MCI due to DLB is recommended, mainly because of the potential clinical utility of the typical finding of hypometabolism in occipital cortex although the experts recognized that formal evidence to support its use in this condition is still poor (Arbizu et al. 2018). However, DAT SPECT, ¹⁸F-DOPA PET, or ¹²³I-MIBG cardiac scintigraphy should be considered in first instance as being more informative investigations (Nobili et al. 2018).

Frontotemporal lobe degeneration (FTLD) is the third most common cause of dementia, following AD and DLB, and is a frequent type of early-onset dementia (Vieira et al. 2013). FTLD in its behavioral variant (bvFTLD) usually has an insidious onset, with progressive deficits in behavior, executive function, and language, and patients may be identified in the initial phases of the disease, before the emergence of dementia. In this group of patients, even at the MCI stage, FDG-PET usually shows orbitofrontal, frontal mesial and lateral, anterior cingulate, insular, and temporal lateral hypometabolism, while metabolism in medial temporal cortex and posterior cingulate is generally preserved (Raczka et al. 2010). These findings, sometimes associated with hypometabolism in the cerebellar hemisphere contralateral to the side of frontal damage (so-called crossed diaschisis), may be helpful in the differential diagnosis with AD dementia. However, in some case the differential diagnosis may be difficult, especially for the frontal variant of AD. Indeed, in these cases, FDG-PET may present the same pathophysiological ambiguity as the clinical symptoms. Amyloid PET may be a useful tool to achieve differential diagnosis, and it will be extensively discussed in the dedicated chapter.

Although full formal evidence on the clinical utility of FDG-PET to support the diagnosis of MCI due to bvFTLD is lacking (Arbizu et al. 2018), semiquantitative assessment of FDG-PET scans correctly identified MCI subjects who develop bvFTLD at follow-up (Perani et al. 2014), even if the number of subjects was very limited. Visual analysis of FDG-PET in MCI patients has been shown to be of limited use in differentiating between MCI patients developing AD and those developing bvFTLD (Grimmer et al. 2016). On the other hand, voxel-based analysis has shown high accuracy in correctly identifying MCI patients who later develop different neurodegenerative disease, including AD, bvFTLD, and DLB (Cerami et al. 2015). Thus, the clinical usefulness of FDG-PET to support the diagnosis of MCI due to bvFTLD has been recommended (Nobili et al. 2018), because of the presence of the known typical metabolic pattern in bvFTLD, already present at MCI stage, mainly including hypometabolism in at least one of the frontal lobes, the anterior temporal lobe, anterior cingulate gyrus, insula, amygdala, and caudate nuclei (Fig. 12.5). This is particularly relevant for the identification of the so-called bvFTLD phenocopies, i.e., patients with clinical symptoms that mimic bvFTLD (Gossink et al. 2016), usually of psychiatric pertinence, but with normal FDG-PET imaging (Kipps et al. 2009).

Vascular dementia (VaD) is a frequent form of dementia, even if several controversies remain, both for the terminology and the pathophysiological mechanisms. For instance, significant neuropathological and clinical overlap between AD and VaD has been found, especially in the early stages (Seo et al. 2009). FDG-PET may help confirm the vascular basis of cognitive impairment, mostly by excluding the presence of typical hypometabolism patterns of AD or of other types of degenerative dementia. However, patients with cognitive impairment of vascular origin may show scattered areas with reduced metabolism possibly extending over cortical and subcortical structures (Seo et al. 2009).

As discussed, FDG-PET has high accuracy in classifying MCI patients of different etiologies. However, the clinical application of research findings is still limited by the lack of standard analytical procedures. In each nuclear medicine unit around the world, scans are evaluated in very different ways, ranging from simple visual assessment to sophisticated computer-assisted comparisons with normal databases, either already available in workstations and supplied by the manufacturer or locally established. Several automatic software tools for the analysis of individual FDG-PET scans are freely available on the web (such as SPM and “Neurostat”) or delivered by industry (such as BRASS^®). “Neurostat” is based on “stereotactic surface projection” (SSP), and it has been developed and applied to patients with AD or questionable dementia (Minoshima et al. 1995) allowing very high accuracy in differentiating AD patients from controls. Topographic estimation of hypometabolic sites is allowed, showing the degree of hypometabolism in comparison with normal controls in terms of Z scores. The spatial normalization procedure is a critical step when using automatic software (such as “Neurostat” and SPM, for instance). Indeed, in order to compare a scan with others, individual’s brain shape and structures must be distorted into a common topographical space. The best approach to achieve spatial normalization is to use a co-registered MRI of the same subject. However, this procedure may be time-consuming if the MRI has not been acquired simultaneously with the PET. New PET-MRI devices can overcome this limitation, allowing precise normalization also in clinical settings. Finally, predisposed normative databases embedded in common automatic tools may not be adequate for age span, number of subjects, or acquisition modalities. Moreover, the quality of locally prepared normal databases relies on selection criteria for normality, whose definition is not trivial.

AD-related hypometabolism can be objectively measured by voxel-by-voxel analysis, and global indices can be provided. The most common tools are the PMOD (PMOD Technologies) Alzheimer discrimination analysis tool (PALZ) (Haense et al. 2009), an AD-related hypometabolic convergence index (HCI) (Chen et al. 2011b), and an average metabolism computed on a set of meta-analytically derived ROIs reflecting an AD hypometabolism pattern (metaROI) (Landau et al. 2011). PALZ and HCI computation is based on the comparison of individual FDG-PET with a reference dataset of healthy controls by voxel-wise t-test. The PALZ score reflects the voxel-by-voxel sum of t-scores in a predefined AD-pattern mask. HCI represents the inner product of individual z-map in a predefined AD z-map. Both PALZ and HCI have shown good accuracy in differentiating AD patients from healthy age-matched controls (Haense et al. 2009; Chen et al. 2011b).

HCI may be able to predict the development of dementia in MCI patients, while metaROI was sensitive in detecting the longitudinal cognitive and functional modification in both MCI and AD patients (Landau et al. 2011). PALZ, HCI, and metaROI ability in distinguishing controls MCI and AD patients has been investigated, showing roughly comparable results between the three methods. Accuracy of classification in each clinical group varied more as a function of dataset than by technique. All techniques were differentially sensitive to disease severity, with a classification accuracy for MCI due to AD to moderate AD varying from 0.800 to 0.949 (PMOD Alzheimer tool), from 0.774 to 0.967 (metaROI), and from 0.801 to 0.983 (HCI) (Caroli et al. 2012).

Finally, support vector machine (SVM) analysis on FDG-PET meta-volumes of interest (metaVOI) has recently shown high accuracy in identifying MCI patients converting to AD (Pagani et al. 2017a, 2015; De Carli et al. 2019). Indeed, it has been proposed that automatic volumetric region-of-interest classifier, based on SVM approach, performs better than the voxel-based methods in differentiating patients with MCI due to AD from healthy controls (Brugnolo et al. 2019). Moreover, independent component analysis on FDG-PET metaVOIs has shown progressive disintegration of functional brain connectivity with progression of cognitive decline in MCI patients, especially those converting to AD dementia (Pagani et al. 2017b).

5.3 Combined Use of FDG-PET and Other Biomarkers

FDG-PET is one of the currently accepted imaging biomarkers for the pre-dementia diagnosis of AD, especially for its high sensitivity in the early diagnosis of AD (Salmon et al. 2009; Dubois et al. 2007). However, FDG-PET accuracy in detecting AD pathology in MCI patients is high but not complete; thus to combine it with other biomarkers or with neuropsychological (memory) test scores may be helpful. Good discriminative performance has been achieved by combining neuropsychology, PET and/or APOE genotype, and CSF biomarkers (Mosconi et al. 2008) and by combining neuropsychology and MRI atrophy analyses (Visser et al. 1999). Moreover, the combined use of neuropsychological testing (visuospatial construction skills), MRI-based hippocampal volume, and posterior cingulate hypometabolism resulted in 96% specificity and 92% sensitivity in identifying MCI patients developing dementia within 12 months (Ottoy et al. 2019).

A study with the combined use of FDG-PET, neuropsychology, and a memory complaint questionnaire has shown that patients with low awareness of memory deficit show a more severe hypometabolic pattern, typical of AD; thus they could have higher risk of developing dementia (Nobili et al. 2010). MCI patients with normal FDG-PET have a low risk of progression within 1 year, despite the presence of a severe memory deficit on neuropsychological testing (Anchisi et al. 2005), thus suggesting a very high negative predictive value of FDG-PET. However, aMCI patients showing a decline of cognitive performance over time, without developing dementia, had a more severe left MTL hypometabolism in comparison with cognitively stable aMCI (Pagani et al. 2010).

Subjects with prodromal AD may have FDG-PET heterogeneity. Similarly to what is shown by means of perfusion SPECT, LOAD patients exhibit more severe hypometabolism in MTL, while EOAD patients show more severe hypometabolism in posterior association neocortex (Kim et al. 2005). Usually, APOE ε4 allele carriers have more severe hypometabolism than noncarriers, despite a similar degree of cognitive impairment (Drzezga et al. 2005). Education has a protective role against symptoms onset in AD. However, when symptoms appear, patients with higher education show more severe hypometabolism in comparison with patients with lower education (Garibotto et al. 2008). A role for oxidative stress in brain glucose alteration in AD patients has been suggested. Indeed, in a mixed group of subjects ranging from subjective memory complaints to mild AD dementia, a significant correlation between left temporal lobe glucose metabolism and plasma activities of extracellular superoxide dismutase, a measure of oxidative stress, has been found (Picco et al. 2014).

Topographical pattern of hypometabolism seems to follow brain amyloid deposition with temporal delay in AD patients (Förster et al. 2012). However, neither amyloid PET nor CSF amyloid marker is able to discriminate short-term converters from non-converters (Ottoy et al. 2019), although other studies assessing the combination of FDG-PET and CSF markers in AD (Landau et al. 2010; Choo et al. 2013; Fellgiebel et al. 2007) stated the utility of brain metabolism in adding a predictive information of conversion to CSF markers as taken alone. In fact, regarding the predictive value toward conversion to AD dementia, FDG-PET has the same value as total Tau (t-Tau) (Choo et al. 2013), as marker of neurodegeneration. Also, Lange et al. (2017) meaningfully followed a stepwise order of biomarkers (viz., ADAS-13 score, hippocampal volume (HV), CSF, and FDG-PET) and showed that CSF p-Tau provided the best incremental risk stratification when added to ADAS-13 score with respect to HV and FDG-PET; in the same study, FDG-PET used in the second step outperformed HV in MCI subjects with relatively preserved cognition (ADAS-13 score <18). Anyway, studies including ¹⁸F-FDG-PET with CSF Aβ_1-42, and MRI, showed a very good prediction of AD dementia conversion in MCI patients, with FDG-PET having the highest added value to clinical and imaging parameters (Garibotto et al. 2017).

Taken together such findings suggest a role of ¹⁸F-FDG-PET both as a diagnostic biomarker and as a predictor of conversion to AD dementia in MCI patients, with high correspondence with cognitive level and neurodegeneration and a moderate added value when combined with other imaging or fluid biomarkers.

6 Amyloid PET and Emerging Tracers

6.1 Amyloid PET Imaging

Although the etiology of AD has not been definitively established, converging evidence suggests that an amyloid precursor protein derivative, beta amyloid (Aβ), may play an important role in the pathogenesis of the disease and be an early event on the path to dementia. The pathogenetic pathways leading to AD are certainly very complex and involve different mechanisms such as the dysfunction in cholinergic neurons and the aberrant aggregation of hyperphosphorylated tau protein. However, the “amyloid cascade” hypothesis remains the most prominent one, according to which sufficient accumulation of Aβ carries significant biochemical, histological, and clinical changes in the pathogenesis of AD (Sanabria-Castro et al. 2017). Moreover, accumulation of Aβ fibrils in the form of “classic” (i.e., neuritic) amyloid plaques is one of the hallmarks of the disease and a key component of the neuropathological criteria for autopsy-based confirmation of diagnosis. Furthermore, assessment of brain Aβ amyloidosis has gained a pivotal role in the diagnosis of AD in vivo, according to the last National Institute of Aging-Alzheimer Association (NIA-AA) (McKhann et al. 2011; Albert et al. 2011) and the International Working Group-2 (IWG-2) criteria (Dubois et al. 2014).

During the past two decades, major breakthroughs have focused on identifying diagnostic biomarkers able to demonstrate the presence of pathological mechanisms of AD and to predict further cognitive decline and dementia onset since the stage of MCI. In this setting, amyloid imaging has established itself as an important neuroimaging tool for the investigation of brain aging and dementia alongside MRI and FDG-PET. In contrast to techniques designed to indirectly estimate levels of brain amyloid plaques from Aβ levels in CSF or plasma, imaging techniques utilizing radiolabeled PET tracers that bind to the aggregated Aβ peptides in amyloid plaques have the potential to directly assess regional brain amyloid plaque pathology (Pontecorvo and Mintun 2011). In other words, amyloid imaging enables the detection and quantification of pathological protein aggregations in the brain.

Together with cerebrospinal fluid (CSF) amyloid-β_1–42 reduction, brain amyloid accumulation identified by PET technology (AMY-PET) is considered one of the biomarkers able to identify the prodromal stage of AD (Jack et al. 2018).

AMY-PET results must be evaluated on the basis of the patient’s medical history, physical examination, and cognitive testing because a positive scan identifies only the presence of brain amyloidosis and does not necessarily correspond to the presence of AD. In fact, amyloid deposition can also occur in normal aging (Aizenstein et al. 2008), and up to 35% of cognitively normal older people have positive AMY-PET. Pathological studies cannot provide information about events early in the disease process or determine how key events are temporally related to each other. The incidence of isolated amyloid-positive marker reaches its maximum at age 70, and then it lowers in older ages with the increase of combination of amyloid and neurodegenerative markers in normal subjects (Jack et al. 2017). Due to the absence of a proven effective disease-modifying drug for AD (Holmes et al. 2008; Cummings et al. 2019), the amyloid hypothesis is being increasingly challenged. Therefore, amyloid imaging allows to readdress questions about the relationship between Aβ aggregation and AD that have not been definitively answered by postmortem studies, such as the significance of Aβ aggregation in cognitively normal individuals and the relationship between the distribution and burden of amyloid pathology and clinical features of AD.

Various compounds have been developed for the imaging of amyloid: radiolabeled Aβ peptide antibodies and peptide fragments, small molecules (derivatives of Congo red, thioflavin, stilbene, and acridine) for PET and SPECT imaging, and putrescine-gadolinium-amyloid-β peptide for MRI. Nonetheless, these compounds failed to provide a direct visualization of amyloid and tau proteins in humans, in consideration of the poor passage across the blood–brain barrier, inadequate brain permeability, and/or low affinity to Aβ aggregates (Quigley et al. 2011). The most widely researched and best validated PET tracer imaging approach to date has utilized ¹¹C-PIB. PIB binds specifically to extracellular and intravascular fibrillar Aβ deposits. At PET tracer concentrations, PIB does not appreciably bind to other protein aggregates (e.g., neurofibrillary tangles), but it binds nonspecifically to WM, likely due to delayed clearance of the lipophilic compound from WM (Quigley et al. 2011). Nonspecific WM retention, however, does not differ between AD and normal controls (Villemagne et al. 2012). PIB amyloid load shows a negative correlation with CSF levels of Aβ_1-42, the other in vivo marker of Aβ pathology, and a positive one with in vitro measures of Aβ pathology found at autopsy (plaques and vascular amyloid) (Rabinovici et al. 2008). PIB has proven to be a sensitive marker for underlying Aβ pathology in cognitively normal older individuals and patients with MCI due to AD or AD dementia. It can detect pathology in patients with early or atypical symptoms as well as in asymptomatic older adults, and it also provides in vivo insight about the dynamic relationship between amyloid deposition, clinical symptoms, and structural and functional brain changes in the continuum between normal aging and AD (Quigley et al. 2011). Nevertheless, more rigorous work is needed to establish the quantitative relationship between PIB binding and Aβ pathology at various disease stages. PIB binding may reach a ceiling at high plaque density and fail to capture the progression of pathology beyond a certain disease stage. Moreover, PIB binds only to fibrillar Aβ, and apparent dissociations between PIB uptake and other disease measures may be due to soluble forms of Aβ that are not detected by PiB-PET (Rabinovici et al. 2008).

A number of additional ¹¹C-labeled PET tracers have been studied in humans as potential amyloid imaging markers. However, the short half-life of ¹¹C is prohibitive in terms of adopting ¹¹C-PIB in clinical practice and limits its availability for research. In the last years, the large body of evidence available on the usefulness of ¹¹C-PIB has been followed by a growing number of publications on the ¹⁸F-labeled compounds for amyloid PET imaging allowing broader application of amyloid imaging to clinical practice and research. In particular, three fluorinated tracers, the stilbene derivative ¹⁸F-florbetaben (NeuraCeq^®), ¹⁸F-florbetapir (Amyvid^®), and ¹⁸F-flutemetamol (Vizamyl^®), have been approved by both the US and the European authorities (Minoshima et al. 2016). All such tracers performed comparably to ¹¹C-PIB in preliminary studies in AD dementia, MCI, and control cohorts. In fact, despite a slight difference in retention in both GM and WM between these radiotracers, binding sites are substantially the same (Ni et al. 2013); thus all the radiopharmaceuticals may be considered similar in the estimation of cerebral amyloid plaques density.

With the precious support of semiquantitative measures, AMY-PET could assess not only brain amyloid load but could also provide tools for longitudinal assessment, progression, or therapeutic effects. To this purpose, the greatest accuracy and precision would be achieved by true quantification techniques. However, the acquisition times and arterial sampling make these methods scarcely applicable in clinical practice. As for semiquantification of amyloid tracer uptake within the brain, Lopresti et al. (2005) assessed a simplified normalization method, i.e., the standardized uptake value ratio (SUVR). SUVR involves the normalization of a standardized uptake value image to a reference region whereby the standardized uptake value image is divided by the mean uptake in the reference region. Cerebellar GM is used as a reference region for ¹¹C-PiB-PET normalization. SUVR normalization is performed on static or summed frames and does not require arterial sampling or dynamic scans, providing similar discrimination between subjects diagnosed with AD and normal controls as compartmental and reference region-based graphic methods. SUVR has been validated both on visual reading and on autoptic studies (Thurfjell et al. 2014) and has become the most widespread technique for normalization of ¹¹C-PiB-PET because of its simplicity and ease of clinical implementation. However, SUVR is known to be affected by several intrinsic and technique-dependent factors such as scanning time window and blood flow (Cselényi and Farde 2015). Furthermore, a univocal agreement on the best method for drawing target ROIs and choosing reference ROIs for amyloid status evaluation has not been reached yet. With the “Centiloid Project” Klunk et al. (2015) tried to harmonize SUVR values on a common scale in order to standardize the method of analyzing PiB-PET data and facilitate direct comparison among different centers and different tracers. Moreover, in order to overcome SUVR limitations, alternative approaches have been attempted, such as the SUVR-independent approach named ELBA (EvaLuation of Brain Amyloidosis) which has been validated for ¹⁸F-florbetapir (Chincarini et al. 2016). This more sophisticated technique uses a ROI-independent method designed to capture intensity distribution patterns rather than actual counts. Another semiquantitative method to measure β amyloid deposition that combines regional dual time-point amyloid PET and MRI data analysis through sophisticated post-processing steps has been recently proposed (Cecchin et al. 2017). In this case, amyloid information is not only corrected for atrophy and spillover errors but also, at least partially, for rCBF dependence. Actually, rCBF and regional cerebral metabolism seem to be tightly coupled in resting conditions, and rCBF and early-phase AMY-PET are highly correlated (Devous et al. 2014). For this reason, visual assessment and semiquantification techniques could be integrated with the information deriving from early AMY-PET acquisition as a proxy of brain perfusion images and a surrogate marker of neurodegeneration in order to obtain a single and more accurate index of brain amyloidosis.

Examples of amyloid PET scans with different tracers in patients with AD and control subjects are shown in Fig. 12.6.

6.2 Utility in MCI

Amyloid imaging studies support a model in which amyloid deposition is an early event on the path to dementia, beginning insidiously in cognitively normal individuals accompanied by subtle cognitive decline and functional and structural brain changes suggestive of incipient AD. As patients progress to dementia, clinical decline and neurodegeneration accelerate and proceed independently of amyloid accumulation, which has either reached a plateau or is increasing slowly (Quigley et al. 2011).

While there is considerable debate about the “amyloid hypothesis,” in its traditional form, postulating that amyloid-β is the causative agent in AD, there is consensus that amyloid is a necessary, if not sufficient, condition to trigger downstream effects leading to symptoms of AD, years later (Musiek and Holtzman 2015). AMY-PET imaging with ¹⁸F-labeled compounds has been applied for predicting the probability of conversion from MCI to AD dementia. Doraiswamy et al. (2014) found that all MCI subjects with positive amyloid PET scan showed greater cognitive and global deterioration over a 3-year follow-up as compared with subjects with negative amyloid PET scan. These results were subsequently confirmed in a larger series of 618 patients with MCI possibly due to AD (Pontecorvo et al. 2017). Other authors (Jack et al. 2010) recommend the integrated use of AMY-PET and MRI to aid to predict progression to AD, combining the high sensitivity of hippocampal atrophy with the specificity of PiB-PET.

Amyloid imaging can also potentially identify patients with MCI who already show Aβ aggregation (thus, in the early clinical phase of AD) and separate them from patients with alternative causes for cognitive impairment (Morris et al. 2001). Such separation may have prognostic implications, assuming that people with MCI and an underlying AD pathology are at higher risk of progressing to dementia. Moreover, these patients may be good candidates for inclusion in clinical trials for AD-specific therapies, allowing these treatments to be tested when they may have a higher likelihood of success (Villemagne et al. 2012).

However, potential protective factors for AD, such as cognitive reserve, which may modulate the symptoms and modify the relationship between Aβ pathology and clinical expression of cognitive impairment, must be taken into account. If one considers average mean PIB uptake in MCI patient groups, they show intermediate values between AD patients and controls. However, this results from a bimodal distribution of PIB uptake in most studies, with a majority of patients demonstrating AD-like uptake levels, a minority showing low-control level binding, and a small number of patients in the intermediate range (Lopresti et al. 2005; Kemppainen et al. 2007; Pike et al. 2007; Rowe et al. 2007; Forsberg et al. 2008; Jack et al. 2008b; Mormino et al. 2009; Wolk et al. 2009). Patients meeting criteria for aMCI are more likely to be PIB-positive than patients with naMCI; PIB positivity is also more common in APOE ε4 carriers compared to noncarriers (Quigley et al. 2011). A meta-analysis (Ossenkoppele et al. 2015) showed that the prevalence of amyloid on PET decreased with age in participants diagnosed with AD (greatest in APOE ε4 noncarriers) and increased with age in most non-AD dementias suggesting that amyloid imaging could be most helpful for differential diagnosis in early-onset dementia, particularly if the goal is to rule in AD dementia. On the other hand, the high concordance between PET and pathology suggests that amyloid imaging might have the potential to be used to rule out AD dementia regardless of age. Furthermore, amyloid in non-AD dementia may be clinically important as amyloid positivity was associated with worse global cognition.

6.3 Combined Use of Amyloid PET and Other Biomarkers

The use of AD biomarkers in routine clinical practice should be chosen on the basis of both their diagnostic performances and cost-effective value. Despite CSF Aβ_1-42 levels and in particular Aβ_1-42/1-40 ratio well correlate with amyloid load on PET (Niemantsverdriet et al. 2017; Hansson et al. 2018), CSF Aβ levels changes may precede brain amyloid deposition (Palmqvist et al. 2016). On the other hand, brain amyloid load on PET still continues to increase even after the onset of cognitive symptoms (Farrell et al. 2017); moreover, in the case of borderline CSF results, AMY-PET can provide a 35% added diagnostic value (Weston et al. 2016). In order to guide clinicians to accurately apply AMY-PET in cognitive decline clinical evaluation, Alzheimer’s Association and the Society of Nuclear Medicine and Molecular Imaging (SNMMI) recommend the use of AMY-PET only in patients with clear cognitive deficits measured by a dementia specialist but in presence of diagnostic uncertainty (Johnson et al. 2013). International Working Group (IWG)-1 (Dubois et al. 2007) and IWG-2 (Dubois et al. 2014) and National Institute of Ageing Alzheimer Association (NIA-AA) criteria (Albert et al. 2011) proposed partially different sets of biomarker abnormalities to identify MCI at higher risk to convert to dementia. However, a univocal sequence order of biomarkers has not been fully established yet (Guerra et al. 2015) and is today matter of consensus conferences between experts (Boccardi et al. 2020).

Recently, NIA-AA diagnostic framework (Jack et al. 2018) grouped biomarkers—including imaging and biofluids—into those of β amyloid deposition (A), pathologic tau (T), and neurodegeneration (N). This ATN classification system shifted the definition of AD in living people from a syndromal to a biological construct and defined AD as a unique neurodegenerative disease characterized by abnormal protein deposits, among different disorders potentially leading to dementia. In this context, AMY-PET, FDG-PET, MRI, and CSF biomarkers (Aβ_1-42, T-tau and P-tau) play a complementary role in the complex field of AD biology and multifactorial etiology of dementia.

Future efforts should be focused on optimizing available resources in order to obtain a more appropriate patient treatment and management.

6.4 Tau PET imaging

The presence of neurofibrillary tangles (NFTs) is another pathological hallmark of AD. NFTs consist of abnormal tau protein which has several isoforms with different ultrastructural conformations participating to the development of intracellular tau aggregates.

Abnormal tau aggregation is mainly considered to be a downstream event after Aβ deposition in AD. Therefore, tau protein deposits are not frequent in the neocortex, even if widespread amyloid-β deposits in neocortex exist already in asymptomatic and very early stages of AD (Morris and Price 2001).

Pivotal studies conducted with tau PET imaging confirmed the already known spatiotemporal pattern of Tau deposition in AD, starting from entorhinal cortex to hippocampus and then extending to the rest of temporal lobe and eventually to neocortical regions (Braak and Braak 1997). Moreover, tau deposition has a close association with brain atrophy, cognitive decline, and severity of dementia symptoms, suggesting the high potential of tau PET imaging as a neuropathological imaging biomarker able to provide not only diagnostic but also prognostic information (Ossenkoppele et al. 2016).

Recently, different Tau-selective PET tracers have been developed and used for human studies: ¹⁸F-THK523, ¹⁸F-THK5117, ¹⁸FTHK5105, ¹⁸F-THK5351, ¹⁸F-AV1451 (T807), and ¹¹C-PBB3 (Okamura et al. 2016). However, functional imaging of NFTs has to face several technical problems including intracellular location and lower concentration of NFTs with respect to Aβ deposits; thus, by including large hydrophilic fractions in their structural formula, radiotracers are able to cross cell membranes and have high affinity in selectively binding tau isoforms, but not Aβ (Villemagne and Okamura 2014). Moreover, although some of these compounds have already been tested in clinical trials (up to phase 3), all the available tracers are currently still in an exploratory stage, and the available literature is limited. Current status and future directions of tau PET tracers in neurodegenerative conditions is summarized in Okamura et al. (2018).

6.5 Receptor Imaging

In the last years, several PET radiopharmaceuticals have been studied for receptor imaging in neurodegenerative diseases. PET tracers for acetylcholine (Ach) receptors and acetylcholinesterase (AchE) have been especially studied. Although interest toward the cholinergic hypothesis in AD has considerably decreased over the last decade because of the poor efficacy of the current AchE inhibitor treatments, evidences showing that cholinergic cell death in the basal forebrain could precede the formation and spreading of Aβ aggregates in the cortex (Schmitz et al. 2016), recently renewed the attention to Ach receptor imaging compounds in studying AD. C-11 labeled tracers, such as N-[¹¹C]methyl-piperidin-4-yl propionate (PMP) and N-[¹¹C]methylpiperidin-4-yl acetate (MP4A), have consistently showed reduced AchE activity in early AD in several cortical areas (Kuhl et al. 1999), further reduced by the acute administration of AchE inhibitors, such as donepezil (Bohnen et al. 2005) and galantamine (Darreh-Shori et al. 2008). These tracers have been proficiently used to demonstrate the severe cholinergic deficit also in Parkinson’s disease, even before dementia, as well as in DLB (reviewed in (Nobili et al. 2011)). However, despite several attempts to identify a reliable tracer for clinical purpose, these tracers remain in the research domain and are mainly C-11 labeled radiopharmaceuticals.

More recently, the fluorinated tracer fluoroethoxybenzovesamicol (FEOBV) has been demonstrated to have an excellent binding specificity for the vesicular Ach transporter in vivo. PET imaging with ¹⁸F-FEOBV allowed to quantify and map the brain cholinergic denervation at a single patient level, as a function of the clinical severity (Aghourian et al. 2017), in a cohort of AD patients. On the other hand, the postmortem observation that nicotinic rather than muscarinic Ach receptors are profoundly affected in AD (Flynn and Mash 1986), promoted the identification of selective nicotinic receptor imaging probes including ¹¹C-nicotine, the fluorinated compound 2-[fluoro-3-(2(S)-azetidinylmethoxy) pyridine (¹⁸F-2FA) or the newer ¹⁸F-AZAN and ¹⁸F-flubatine, showing promising results (see the review by (Bao et al. 2017)).

7 Conclusions and Perspectives

This review has tried to underline that there is no correct diagnosis in the field of MCI without the “rational” use of both morphological and functional neuroimaging. Thus, due to the current shift from a clinical to a biological conception in the diagnosis of neurodegenerative diseases, a biomarker-based approach involving different biological measures is paramount as it can reflect pathological changes in vivo. Yet, the areas of uncertainty remain in several instances. In the case of a healthy middle-age subject with brain amyloidosis but without signs of neurodegeneration, we still have too short follow-up studies to say whether all these subjects will develop AD dementia, or a part of them will never develop it, thanks to protective factors, such as brain plasticity/brain reserve, diet, lifestyle, etc. In other cases of MCI of suspected Alzheimer’s disease nature, neuroimaging findings do not match CSF findings, and only the clinical follow-up (or new upcoming biomarkers) will disclose the fate of that individual. Such issues are even more complicated according to the most recent pathological evidences in which different proteinopathies, alone or in combination, might underlie the same clinical and even imaging presentation (Nelson et al. 2019).

Neuroimaging has now entered the routine practice, and further development will reinforce our diagnostic accuracy. Along with the validation of new radiotracers for receptor imaging, future efforts should be directed toward optimization of functional MRI techniques as well as quantitative and semiquantitative tools for PET imaging interpretation and to develop new tracers able to detect proteinopathies other than amyloidosis. Moreover, we hope we will have the new MRI-PET equipment available at reasonable costs. Also, we need the integration between neuroimaging findings and fluid biomarkers not just in Alzheimer’s but also in other neurodegenerative diseases.

In conclusion, there is urgent need of worldwide harmonization and standardization of biomarkers, assessing and increasing their performance and accuracy in detecting early disease, and of the development of diagnostic algorithms comprising combinations of biomarkers; the final aim is to develop shared clinical guidelines to follow in advanced memory clinics facing the dilemma of etiologic diagnosis in MCI patients (Frisoni et al. 2017). We also need physicians able to dialogue on a common ground among neurologists, psychiatrists, and geriatricians, on the one hand, and neuroradiologists and nuclear medicine physicians, on the other hand. As the life expectancy of the western population is expected to increase, the field of cognitive disorders of the elderly will become a new medical specialty. This new “dementologist” should have advanced knowledge of neuroimaging tools and must be able to properly manage all available biomarkers.

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Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
Federico Massa, Caterina Lapucci, Agnese Picco, Dario Arnaldi, Luca Roccatagliata & Flavio Nobili
IRCCS Ospedale Policlinico San Martino, Genoa, Italy
Matteo Bauckneht, Selene Capitanio, Dario Arnaldi, Luca Roccatagliata & Flavio Nobili
Sezione di Genova, Istituto Nazionale di Fisica Nucleare, Genoa, Italy
Enrico Peira & Andrea Chincarini
Department of Health Sciences (DiSSAL), University of Genoa, Genoa, Italy
Luca Roccatagliata

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Federico Massa
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Flavio Nobili
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Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
Rudi A. J. O. Dierckx
Faculty of Electrical Engineering, Medical Engineering and Computer Science, Offenburg University, Offenburg, Germany
Andreas Otte
Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
Erik F. J. de Vries
Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
Aren van Waarde
Neurology, University Medical Center Groningen, Groningen, The Netherlands
Klaus L. Leenders

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Massa, F. et al. (2021). Neuroimaging Findings in Mild Cognitive Impairment. In: Dierckx, R.A.J.O., Otte, A., de Vries, E.F.J., van Waarde, A., Leenders, K.L. (eds) PET and SPECT in Neurology. Springer, Cham. https://doi.org/10.1007/978-3-030-53168-3_12

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DOI: https://doi.org/10.1007/978-3-030-53168-3_12
Published: 11 October 2020
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Neuroimaging Findings in Mild Cognitive Impairment

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