Degree project FDG PET and MRI as Biomarkers of Tau
Pathology in Alzheimer’s Disease
Master Degree Project (60 credits) in Cognitive Neuroscience
Second Cycle 30 credits Spring term 2021
Student: Putu Ayuwidia Ekaputri Supervisor: Alexis Moscoso Rial1 Tora Dunås1
Sakari Kallio2
Examiner: Andreas Kalckert2
1Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
2University of Skövde, Skövde, Sweden
FDG PET and MRI as Biomarkers of Tau Pathology in Alzheimer’s Disease Abstract
Fluorodeoxyglucose Positron Emission Tomography (FDG PET) and Magnetic Resonance Imaging (MRI) are commonly used in a clinical setting as an examination in Alzheimer’s Disease (AD) patients. FDG PET is an imaging tool to evaluate hypometabolism; meanwhile, the MRI observes the brain volume. However, it is still unclear which examination better reflects the tau tangles, which have been known as the hallmark’s pathology of AD. Therefore, this study was conducted to compare FDG PET and MRI in assessing tau pathology in AD, by utilizing a database from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The presence of tau tangles was confirmed by using the Tau-PET images. In total, 275 cognitively impaired subjects were included as well as a subgroup of 147 subjects with positive amyloid status. Based on the analysis, it was observed that higher Tau-PET is significantly associated with FDG PET hypometabolism and MRI atrophy. A similar result was also seen in the amyloid positive subgroups. By comparing the spearman’s correlation coefficients, it was found that that the correlation was stronger between Tau PET and FDG PET (r=-0.414, p<0.001, and r=-0.475, p<0.001 in the positive amyloid subgroup) compared to Tau-PET and MRI (r=-0.331, p<0.001 and r=-0.440, p<0.001 in the positive amyloid subgroup). In conclusion, FDG PET better reflects the tau pathology compared to MRI in AD.
Keyword: alzheimer’s disease, fdg pet, mri, tau pet, tau pathology
I. Introduction 1.1 Alzheimer’s Disease
Alzheimer's disease (AD) is the commonest neurodegenerative disease which results in declining cognitive ability and dementia in older people (Kumar, Sidhu, Goyal, & Tsao, 2021).
This disease usually affects people over 65 years of age. Currently, 24 million people have AD worldwide, many of them are elderly, and this disease's risk increases with age. AD prevalence is estimated to increase four times by 2050. Incidence rates of AD are slightly higher for women, especially after 80 years of age (Beam et al., 2018). The economic burden due to the high living cost of AD patients becomes one of the major problems in developed countries (Thakur, Kamboj, Goswami, & Ahuja, 2018; Tiwari, Atluri, Kaushik, Yndart, & Nair, 2019).
AD is primarily characterized by loss of memory function, inability to learn new things, loss of language function, and impaired perception of space, among others (Kumar et al., 2021). Apart from these neuropsychological symptoms, there are also psychiatric manifestations such as depression, aggression, anxiety, delusions, and others (Zhao et al., 2016). All these symptoms affect the patients' social functioning and make it difficult for them to carry on daily living, progressively worsening over several years and resulting in complete disability. By now, no treatment for AD is currently available, but some managements such as disease-modifying treatments (DMTs) are developed to improve symptoms in the early stage (Kumar et al., 2021; Yiannopoulou & Papageorgiou, 2020).
Several changes can be detected in the brain of a person with AD many years before the first symptoms appear. Some of these changes, such as β-amyloid accumulation, are significantly increasing years or even decades before the first symptoms (Gordon et al., 2018).
AD usually starts with episodic short-term memory loss with relative sparing of long-term memory, followed by impairment in cognitive functions such as problem-solving, judgment, executive functioning, lack of motivation, disorganization, multitasking, and abstract thinking (Kumar et al., 2021; Tiwari et al., 2019). AD can be separated into three phases (Albert et al., 2011; Johnson, Fox, Sperling, & Klunk, 2012):
1. Preclinical AD: brain changes due to AD can be detected with biomarkers in cognitively unimpaired individuals
2. MCI due to AD: mild symptoms of dementia with no everyday activities limitation 3. Dementia due to AD
a. Mild: symptoms with some activities limitation b. Moderate: symptoms with many activities limitation
c. Severe: symptoms with limitation in the majority of everyday activities
This progression’s time may vary in every patient, influenced by genetics, age, gender, and other factors (Sperling et al., 2011).
1.1.1 Pathogenesis and Pathophysiology of AD related to Protein Tau
By now, two main neuropathological hallmarks define AD: extracellular deposition of β-amyloid and intracellular aggregation of tau protein (James, Doraiswamy, & Borges-Neto, 2015). In this study, we focus on tau protein pathology in AD.
Tau protein is defined as a Microtubule-Associated Proteins (MAPs) (Muralidar, Ambi, Sekaran, Thirumalai, & Palaniappan, 2020). Normally, tau protein has the primary function to stabilize axonal microtubules, running along neuronal axons and has an essential function for intracellular transport. These microtubules' assembly is held together by the tau protein.
Tau protein can also form interconnecting bridges between contiguous microtubules to form a stable network of microtubules. Tau needs to have a normal phosphorylation level to work optimal stabilizing microtubules. In AD, the tau kinase enzyme activity that works for the phosphorylation process increases, leading to a hyperphosphorylated state and making tau lose its biological activity. Patients with AD have a fourfold risk of having abnormal hyperphosphorylated tau proteins (Mocanu et al., 2008). Instead of binding with microtubules, the tau protein form twisted paired helical filaments known as neurofibrillary tangles (NFTs). Accumulation of NFTs leads to loss of communication between neurons and signal processing and finally cause apoptosis in neurons. The aggregation of abnormal tau proteins was found to be neurotoxic and lead to synaptic plasticity and lack of proper axonal transport (Fan et al., 2019). Furthermore, loss of microtubules stabilization function leads to pathological disorder in the cytoskeleton's structure and monitoring function, which disturbs axonal transportation and would cause synaptic dysfunction and neurodegeneration (Breijyeh
& Karaman, 2020; Muralidar et al., 2020; Tiwari et al., 2019).
1.2. Imaging as Biomarkers in AD
Diagnosis of AD traditionally relies on characterizing clinical phenotypes through a detailed evaluation, including history taking, mental status, and neurologic examination.
Nevertheless, the accurate diagnosis of AD and non-AD neurodegenerative disease is still quite challenging. A clinical-autopsy study of more than 900 cases showed that 40% of patients clinically diagnosed with non-AD had post-mortem histopathology consistent with AD (Beach, Monsell, Phillips, & Kukull, 2012). On the other hand, about 30% of the patients who are thought to have AD clinically do not meet post-mortem pathologic criteria for AD. Thus, this makes it difficult to determine if there is a management failure. The management failure was
probably due to ineffective drug or diagnostic errors since some of patients diagnosed with AD did not show post-mortem pathologic criteria of AD (James et al., 2015; Jie, Treyer, Schibli, &
Mu, 2021). Therefore, it is necessary to develop biomarkers as diagnostic tools to detect AD correctly, even in the earliest stage. Neuroimaging techniques is one of the promising candidates as biomarkers to AD. In this study, we focus on the uses of tau positron emission tomography (PET), 18F-fluorodeoxyglucose positron emission tomography (FDG PET), and structural magnetic resonance imaging (MRI).
1.2.1 Tau PET Imaging Examination in AD
PET with tau-specific ligands is a promising modality to assess the differential diagnosis of neurodegenerative disease and disease staging in AD patients. Tau imaging may also have a role as a predictor of future cognitive decline. Despite the challenges inherent in imaging tau, remarkable progress has been made over the past few years. [18F] FDDNP was the first PET tracer developed to target tau. Still, the clinical trial result showed that [18F] FDDNP demonstrates binding to both β-amyloid and tau pathology, by means it was not designed as a specific tau tracer (Leuzy et al., 2019). Due to the lack of in vivo selectivity and specificity of FDDNP, a new set of Tau PET tracers was developed. There are two generations of tau ligand, first-generation and second generation. The first generation called Tauvid, also known as [18F]
Flortaucipir, [18F]AV-1451, and [18F]T807 (Jie et al., 2021). The second generation is now still in development, such as [18F]MK-6240, [18F]RO6958948 (RO-948), [11C]RO6931643 (RO- 643) and [11C]RO6924963 (RO-963) (Wang & Edison, 2019).
Tauvid is the first FDA-approved PET tracer for in vivo imaging of tau aggregate pathology in AD (Jie et al., 2021). Patients with AD present an increase in Tauvid uptake in the brain especially start from the entorhinal cortex in the medial temporal lobe. The spreading continues to the basal and lateral temporal, inferior parietal, posterior cingulate, and finally reach primary cortical regions (Cho et al., 2016). The severity of cognitive dysfunction in AD has been reported to closely relate to the accumulation of tau pathology measured by Tau PET (Cho et al., 2020, 2019; Johnson et al., 2016). Moreover, Tauvid was widely investigated and found to bind the hyperphosphorylated tau protein in post-mortem AD patients selectively (Smith, Wibom, Pawlik, Englund, & Hansson, 2019). Therefore, Tau PET imaging represents a promising diagnostic tool for testing of tau pathology in neurodegenerative disease and also to evaluate the progression of AD.
1.2.2 AD Hypometabolism in FDG PET Examination
FDG PET is extensively and increasingly used to support the clinical diagnosis of patients with suspected neurodegenerative disease, especially AD. FDG PET shows cumulative
losses of neuropil, loss of synapse, which leads to its functional impairment of the neurons (Ou et al., 2019). In AD patients and high-risk individuals, FDG PET has been used to measure the cerebral metabolic rate of glucose consumption (CMRglc), represents the neuronal activity.
Some research suggested the low glucose consumption is related to the severity of cognitive impairment as clinical AD symptoms rarely come without CMRglc decreases (de Leon et al., 2001; Fouquet et al., 2009; Lisa Mosconi, 2013). Low FDG PET signal represents neuronal hypometabolism due to neurodegeneration and consumption of glucose by astrocytes.
Additionally, some studies revealed that FDG PET could also be a biomarker tracking vascular abnormalities in the blood-brain barrier. Individuals with reduced FDG PET brain metabolism show faster rates of cognitive decline compared to individuals without significantly impaired FDG PET uptake (Ou et al., 2019). Patients with diminished FDG PET have much higher rates of progression to AD. This suggests that individuals with diminished glucose metabolism measured by FDG PET are more likely to progress to AD or to be AD. Specifically, in AD patients, the hypometabolism pattern starts from the posterior cingulate cortex, continues to the parietal-temporal areas, and then the frontal regions (Chételat, 2011).
Reduced FDG PET was associated with reduced brain volumes, starts from the medial temporal lobe, extended to parietal association areas, and finally reduced frontal and primary cortices volumes. The major strengths of FDG PET in AD are high sensitivity (72.82%) to differentiate AD from other neurodegenerative diseases, and individuals at higher versus lower AD risk, with good quantitative and topographical correlation with clinical progression (Lisa Mosconi et al., 2010; Ou et al., 2019; Shivamurthy, Tahari, Marcus, & Subramaniam, 2015).
1.2.3 MRI Scan Brain Atrophy in AD
MRI has played a significant role over the past four decades to improve the management of AD. Previous research found there is a correlation between volumetric MRI measurements of regional brain atrophy with the degree and distribution of neurofibrillary tangles (Whitwell et al., 2012). Therefore MRI, which detects brain atrophy, can be a potential marker to evaluate the tau tangles distribution. MRI has relevant safety and accessibility properties and is extensively used in clinical routine and research, especially for understanding the pathophysiology of neurodegenerative disorders (Pini et al., 2016; Schwarz et al., 2019).
MRI can show the progression of cerebral atrophy as a characteristic feature of several neurodegenerative diseases. Dendritic and neuronal losses are suggested to be the major contributors to cerebral atrophy. Brain atrophy assessed on structural MRI has been demonstrated as a valid maker for AD-related neurodegeneration with post-mortem histology (Rabinovici et al., 2007). A review by Frisoni et al. (2010) concluded that high-resolution MRI
(with a spatial resolution of the order of millimetres) accurately quantifies in vivo neurodegeneration of specific cortical and subcortical gray matter regions in terms of volume loss, morphological changes, and cortical thinning, as well as estimates white matter structural damage. In addition, based on research conducted by Byun et al. (2015), by around 59% of patients with hippocampal and cortical atrophy, 19% only have progressive atrophy in the hippocampal. In comparison, 12% of patients had brain atrophy in the cortical. However, 10%
of patients have no atrophy in both regions.
Atrophy of the hippocampus is widely used as a marker to assess the neuro-degradation in AD. This pattern may also determine the stage or progression of AD pathology. Hippocampal volume is involved early and progressively, and it is associated with Braak staging (pathological degree of AD) (Braak & Braak, 1995) and neuronal counts both in Alzheimer's and aging (Apostolova et al., 2015). Several MRI studies reported that hippocampal volume in clinical AD patients was 15% to 40% smaller than healthy controls. Hippocampal volume was already reduced by 15-30% at the mild dementia stage of AD and by 10-15% in the amnestic variant of mild cognitive impairment (Shi, Liu, Zhou, Yu, & Jiang, 2009). Decreased hippocampal volume is associated with the severity of cognitive impairments and episodic memory deficits.
Compared to other neurodegenerative diseases, hippocampal atrophy tends to be greater in AD than in other types of dementia, such as with Lewy bodies or vascular dementia with similar clinical stages (Barber, Ballard, McKeith, Gholkar, & O’Brien, 2000; Burton et al., 2009).
Patterns of gray and white matter changes in the brain assessed in vivo using MRI suggested being useful to diagnose and differentiate both subtype in AD and other neurodegenerative diseases. Additionally, MRI may present cortical atrophy patterns that accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts (Johnson et al., 2012; Persson et al., 2017; Pini et al., 2016).
1.3. Aim of The Present Study
Biomarkers are a promising tool to detect early AD. One of them is the detection of neurofibrillary tangles, a fundamental neuropathological hallmark of AD. Tau pathology has been reported to be closely associated with the decrease of cognitive function (Schöll et al., 2016). The deposits of NFTs have been shown to be related to neuronal loss and cognitive dysfunction in AD (Brier et al., 2016; Giannakopoulos et al., 2003).
Tauvid (18-F AV 14-51) is a radioactive tracer for PET imaging to detect tau pathology in Alzheimer's Disease. However, Tauvid can differentiate the late-stage AD from a normal subject, but it is still limited to evaluating AD early stages (Jie et al., 2021). PET with
Fluorodeoxyglucose (FDG) and structural MRI are commonly used techniques in clinical routine for the assessment of neurodegeneration. FDG PET assessing glucose metabolism is a marker to examine the metabolic differences in neurodegenerative disease. FDG PET is usually considered for evaluating mild cognitive impairment, the early symptomatic stage in AD (Smailagic et al., 2015). On the other side, MRI allows the assessment of atrophy which is increased in AD (Kumar et al., 2021).
However, it is unclear which currently available neurodegeneration marker (FDG PET or structural MRI) better reflects underlying AD neuropathology as determined with Tau PET.
In the clinical setting, it is not common for a patient to get two imaging examination. Using data from individuals enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who underwent concurrent Tau PET, FDG PET, structural MRI, we aim at establishing which marker better mirrors tau pathology in AD.
2. Methods 2.1 Participant
The present study participants were selected from the Alzheimer’s Disease Neuroimaging Initiative phase III, in which subjects were recruited from January 2017 to January 2021. Participants with available Tau PET (using Tauvid or 18-F AV 14-51 radiotracer), FDG PET, and MRI scans within six months were included in this study. From around 1000 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, 291 subjects met the inclusion criteria. We focused on the subjects with cognitive impairment, and the final participant of this study is 275 participants. The images (Tau PET, FDG PET, and MRI) were downloaded from www.adni.loni.usc.edu on February 25th 2021.
2.2 Imaging Acquisition
FDG PET images were acquired 30 to 60 minutes after the injection of 185 MBq (5.0 mCi) ± 10% of [18F] FDG, using a 30-minute dynamic acquisition with 6x5 minute frames.
The Tau PET images were acquired 75-105 minutes post-injection of 370 MBq (10.0 mCi) ± 10%, using a 30-minute dynamic acquisition with 6x5 minutes frames. All the MRI imaging was performed at 3T scanners with a standardized protocol. More details about imaging protocols and preprocessing of PET and MRI images in ADNI are available at
http://adni.loni.usc.edu/methods 2.3 MRI Preprocessing
First, all the images were centred to MNI space and segmented into gray matter (GM), white matter (WM), and CSF using SPM12. Using the DARTEL toolbox in SPM12, a group- specific template was created by integrating the sampled brains for spatial normalization. After creating the group-specific template, all the gray matter T1 MRI images were spatially normalized to the MNI space for quantification of using atlas-based techniques. The normalization process included a modulation step to preserve the amount of tissue and allow volumetric comparisons. This study uses ROIs generated based on previous research that defines the regions typically affected in AD. The ROIs for the MRI images are entorhinal, inferior temporal, middle temporal, and fusiform cortex(Jack et al., 2017).
2.4 PET Preprocessing
Tau and FDG images for each subject were co-registered and re-sliced to the corresponding T1 MRI scans. The images were subsequently spatially warped to the MNI space using the deformation parameters previously derived from the DARTEL-based spatial normalization of T1 MRI images. Standardized uptake value ratio (SUVR) images in MNI space were created by scaling PET intensities in the region of interest (ROI) by the average signal in the inferior cerebellum cortex (Tau PET) or in the pons (FDG PET). This study's ROIs are generated based on previous research, by using a meta-analysis to define the regions that are
typically affected in AD (Landau et al., 2011). The ROIs of FDG are left and right angular, left and right temporal, and post cingulate (Landau et al., 2011). The Tau PET ROIs are entorhinal, amygdala, parahippocampal, inferior temporal, middle temporal, and fusiform cortex (Jack et al., 2017).
2.5 Assessment of Amyloid Status
The amyloid status of the participants was downloaded from the ADNI database and was examined using AV-45 PET and FBB PET. The participant was classified as amyloid positive based on the cut points global amyloid AV-45 PET SUVR > 1.11 and FBB PET > 1.08 as explained in the ADNI protocols.
2.6 Data Analysis
All the analyses were performed using IBM SPSS Statistic version 23. All the participants (n=275) were included in the study to investigate the associations of MRI and FDG PET with Tau PET. In addition, we also investigated these associations in the subgroup of study participants with a positive amyloid status (n=147) as determined with amyloid PET. Scatter plots were created to visualize the relationship between Tau PET and FDG PET and between Tau PET and MRI. Spearman correlations were conducted as the data of the study variables were non-linearly associated.
3. Results 3.1 Subject Characteristics
The current study included 275 cognitively impaired patients from ADNI database. All patients underwent Tau PET, FDG PET, and structural MRI within six months. The age and sex of all the subjects and the subject in the amyloid positive subgroup, are presented in Table 1.
Table 1. Subject Characteristic (mean ± SD). For continuous variables, mean + SD is indicated.
All subjects (n=275) Amyloid positive (n=147)
Age (years) 74.43 ± 7.922* 74.83 ± 7.710
Sex (M/F) 158/117 (57.5%/42.5%) 78/69 (53.1%/46.9%)
*1 subject with unknown age
Table 2. SUVR of Tau PET and FDG PET and Amount of Tissue. The average of cortical SUVR was calculated from PET images. MRI images provided the average amount of tissue. Median with minimum and maximum value is presented in the variable without normal distribution. Mean with standard deviation is shown in the normally distributed variables.
All subjects (n=275)
Amyloid positive (n=147)
Tau PET SUVR p <0.001 p < 0.001
Median 2.145 2.327
Min 1.698 1.795
Max 4.039 4.039
FDG PET SUVR p=0.096 p > 0.200
Mean 1.411 ± 0.200 1.366 ± 0.190
Amount of tissue p=0.066 p > 0.200
Mean 1.211 ± 0.066 1.203 ± 0.065
The distribution of the Tau PET SUVR, FDG PET SUVR, and MRI volumes (amount of tissue) was tested using Kolmogorov-Smirnov tests of Normality. The FDG SUVR and the amount of tissue in all subjects showed normal distribution (p=0.096, p=0.066), while the Tau PET SUVR was not normally distributed (p=<0.001). In the amyloid positive subgroups, the Tau
PET SUVR was also not normally distributed (p=<0.001). On the other hand, FDG SUVR and the amount of tissue showed normal distributions (p > 0.200).
3.2 Correlation Between Tau PET and FDG PET and between Tau PET and MRI We investigated the relationship between Tau PET and FD-PET and between Tau PET and MRI. Figure 1 shows the scatter plot of Tau PET with FDG PET and Tau PET with MRI in 275 subjects. The result showed a non-linearly relationship between tau pathology measured by Tau PET with hypometabolism measured by FDG PET. A similar result is also presented between tau pathology and brain atrophy measured by MRI.
Figure 1. Association between Tau PET and FDG PET (left) and MRI (right). The scatter plot is presenting the best fit line and 95% confidence interval in 275 subjects.
We also investigated the relationship between those biomarkers in amyloid positive sub- group, are presented in Figure 2.
Figure 2. Association between Tau PET and FDG PET (left) and MRI (right) in 147 subjects with amyloid positive, including best fit line and 95% confidence interval.
To measure the correlation’s power, we continued the analysis to calculate the Spearman’s rank correlation coefficients. The result showed a significant correlation between Tau PET and FDG PET, and between Tau PET and FDG PET. We observed that the correlation between Tau PET and FDG PET was higher (r=-0.414, p<0.001) than Tau PET and MRI (r=- 0.331, p<0.001). The correlation between Tau PET and FDG PET was also higher (r=-0.475, p<0.001) than Tau PET and MRI (r=-0.440, p<0.001) in the amyloid positive sub-group. In addition, the correlations were stronger in the sub-group subjects with amyloid positive. The Spearman’s rank correlation coefficients are presented in Table 3.
Table 3. Spearman’s Rank Correlation Coefficients between Tau PET and FDG PET and between Tau PET and MRI.
All subjects (n=275) Amyloid positive (n=147) Tau PET – FDG PET r=-0.414 (p<0.001) r = -0.475 (p<0.001) Tau PET – MRI r=-0.331 (p<0.001) r = -0.440 (p<0.001)
4. Discussion
The accumulation of tau tangles, one of the hallmarks of AD, can be detected using Tau PET imaging. However, Tau PET is not used yet in clinical routine for AD examination. The use of Tau PET as a routine examination for Alzheimer's Disease is not common. Meanwhile, MRI and FDG PET are more widely used as imaging examinations in dementia patients to differentiate the type of dementia in the early stages. However, it is not clear yet which imaging modality better reflects tau pathology in Alzheimer's disease. Identifying which imaging modality more strongly correlates with tau pathology will facilitate the physician to choose the best examination in the clinical setting, especially in the early stages of Alzheimer's Disease.
This study used Tau PET, FDG PET, and MRI data from the ADNI database to identify whether hypometabolism or brain atrophy better reflects tau pathology. By comparing its spearman correlation coefficient, we found that hypometabolism has a stronger correlation with tau pathology than brain atrophy. The correlation between tau pathology and hypometabolism was higher than brain atrophy concurs with previous research conducted in typical and atypical AD (Whitwell et al., 2018). We also add to earlier findings by demonstrating that the correlation between Tau PET and FDG PET was higher than Tau PET and MRI in subjects with elevated levels of amyloid in the brain. This result suggests that FDG PET is more sensitive than structural MRI to detect neurodegeneration due to tau pathology in AD. Besides, considering the temporal course of Alzheimer's disease, several previous studies have shown that hypometabolism can be detected earlier than the presence of volume changes in AD (Kljajevic, Grothe, Ewers, & Teipel, 2014).
The correlation between hypometabolism and tau tangles as one of the hallmark pathologies in Alzheimer Disease has been widely reported. It has been persistently shown that the increase of Tau PET binding was associated with regional hypometabolism in AD (Bischof et al., 2016; Gordon et al., 2019). These reports are consistent with our finding that Tau PET was significantly correlated with FDG, also in subjects with elevated brain amyloid. Previous in vivo studies concluded that the presence of tau pathology was associated with synaptic loss and dysfunction (Kljajevic et al., 2014), therefore, causing the activity of neurons to decrease and resulting in lower uptake of glucose. However, glucose deficit in hypometabolism can also trigger the formation of tau tangles (Lauretti, Li, Di Meco, & Praticò, 2017). It has been established that abnormal cerebral metabolic decrease is one of the early features in AD.
Hypometabolism happens adequately before any clinical manifestations of AD in patients who genetically have a predisposition to AD and in normal subjects with a maternal history of AD (Hunt et al., 2007; L Mosconi et al., 2009). A review by Gibson (2002) concluded that glucose metabolism decreases significantly in patients with MCI who is progressing into AD, lead to tau tangle formation. These findings support the idea that the reduction of cerebral glucose
metabolism may play a crucial role in the early stage of AD pathology and closely associated with tau pathology. However, the molecular pathophysiology mechanism of hypometabolism associated with tau pathology is still unknown. Despite the fact that the relation between tau and hypometabolism remains unclear, our findings suggest that there is a direct relationship between tau tangles and hypometabolism.
Beside hypometabolism, brain atrophy also a critical feature in AD. Cortical thickness negatively correlated to Tau PET uptake, as in our study, the correlation was significant. A similar result reported in previous research using observational case series in six patients showed that Tau PET uptake was highly correlated with cortical atrophy (Xia et al., 2017).
These results support the idea that the deposit of tau tangles is connected to neuronal loss, gliosis, and other signs of neurodegeneration and cognitive dysfunction in AD (Brier et al., 2016; Giannakopoulos et al., 2003). Previous post-mortem research also reported a high correlation between tau tangles and cortical thickness (C R Jack Jr et al., 2002; Whitwell et al., 2012). However, the correlation between tau pathology and brain atrophy, in the current study is lower than hypometabolism. This result may be an indication that the correlation between tau pathology and brain atrophy, though relatively strong, is not direct and may be confounded by other factors. Besides tau tangles, many other factors in the ageing process influence the decrease of brain volume. Oxidative stress, vascular disease, the presence of TDP- 43 protein also related to brain atrophy in AD (Hirai et al., 2001; Josephs et al., 2014;
Rodriguez et al., 2000). Therefore, brain atrophy can be found in the absence of tau tangles.
This is also supported by previous research showing that the correlation between Tau PET and brain atrophy is lower in older participants (Whitwell et al., 2018). In older patients, the ageing process may also be influenced by other comorbidities like hypertension or metabolic disease.
In addition, both correlations are higher in the sub-group subjects with amyloid positive. Minimal cognitive impairment (MCI) patients with amyloid positive status tend to progress faster to be AD than those with amyloid negative (Clifford R Jack Jr et al., 2010; Vos et al., 2013; Ye et al., 2018). Previous research also reported an increase of Tau’s SUVR in AD patients with positive amyloid compared to amyloid negative patients (Smith et al., 2020).
Furthermore, an interaction between tau and amyloid was observed in AD patients. It was shown that the tau-related cognitive reduction was worse in individuals with high amyloid (Aschenbrenner, Gordon, Benzinger, Morris, & Hassenstab, 2018). The decrease of hippocampal volume also affected by the presence of amyloid (Aschenbrenner et al., 2018).
Therefore, it is expected in this study that the correlation between tau and FDG PET and between Tau and MRI is higher in the amyloid positive sub-groups.
The main strength of the present study is the head-to-head comparison of hypometabolism and brain atrophy of the strength of the correlation with tau tangles in a
relatively large number of subjects. Even though these two biomarkers connected to tau pathology, they may have a different role in the progression of tau tangle pathology. A limitation of the present study is that we only used ROI-based analysis. Thus, this analysis could not explain the correlation that may exist in other brain regions. ROI-based analysis may miss out on interesting correlations in different brain areas, as the progression of Alzheimer Disease is different in every region of the brain. Therefore, it will be useful to analyze local correlations throughout the brain.
In conclusion, our findings suggest that hypometabolism better reflects tau pathology than brain atrophy. Based on this result, we suggest using FDG PET, if available, rather than MRI in the clinical settings. Considering the ethical issue of using radioactive in patients, FDG PET must provide benefits to AD. The amount of radioactive injected into the patient should be within a safe range. Based on a review by Bohnen et al. (2012), no safety issues have been found in the studies of FDG PET in AD, AD-related dementia, or other neurodegenerative disorders (i.e., primary progressive aphasia, posterior cortical atrophy). The review also notes that FDG PET is effective and useful in providing more information in assessing patients with dementia (Bohnen et al., 2012). Still, it requires significant research to support this finding, especially in different types and AD stages.
References
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., … Phelps, C. H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease:
recommendations from the National Institute on Aging-Alzheimer’s Association
workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association, 7(3), 270–279.
https://doi.org/10.1016/j.jalz.2011.03.008
Apostolova, L. G., Zarow, C., Biado, K., Hurtz, S., Boccardi, M., Somme, J., … Watson, C.
(2015). Relationship between hippocampal atrophy and neuropathology markers: A 7T MRI validation study of the EADC-ADNI Harmonized Hippocampal Segmentation Protocol. Alzheimer’s & Dementia, 11(2), 139–150.
https://doi.org/https://doi.org/10.1016/j.jalz.2015.01.001
Aschenbrenner, A. J., Gordon, B. A., Benzinger, T. L. S., Morris, J. C., & Hassenstab, J. J.
(2018). Influence of tau PET, amyloid PET, and hippocampal volume on cognition in Alzheimer disease. Neurology, 91(9), e859–e866.
https://doi.org/10.1212/WNL.0000000000006075
Barber, R., Ballard, C., McKeith, I. G., Gholkar, A., & O’Brien, J. T. (2000). MRI volumetric study of dementia with Lewy bodies: a comparison with AD and vascular dementia.
Neurology, 54(6), 1304–1309. https://doi.org/10.1212/wnl.54.6.1304
Beach, T. G., Monsell, S. E., Phillips, L. E., & Kukull, W. (2012). Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010. Journal of Neuropathology and Experimental Neurology, 71(4), 266–273. https://doi.org/10.1097/NEN.0b013e31824b211b
Beam, C. R., Kaneshiro, C., Jang, J. Y., Reynolds, C. A., Pedersen, N. L., & Gatz, M. (2018).
Differences Between Women and Men in Incidence Rates of Dementia and Alzheimer’s Disease. Journal of Alzheimer’s Disease : JAD, 64(4), 1077–1083.
https://doi.org/10.3233/JAD-180141
Bischof, G. N., Jessen, F., Fliessbach, K., Dronse, J., Hammes, J., Neumaier, B., … Initiative, A. D. N. (2016). Impact of tau and amyloid burden on glucose metabolism in
Alzheimer’s disease. Annals of Clinical and Translational Neurology, 3(12), 934–939.
https://doi.org/10.1002/acn3.339
Bohnen, N. I., Djang, D. S. W., Herholz, K., Anzai, Y., & Minoshima, S. (2012). Effectiveness and safety of 18F-FDG PET in the evaluation of dementia: a review of the recent literature. Journal of Nuclear Medicine : Official Publication, Society of Nuclear
Medicine, 53(1), 59–71. https://doi.org/10.2967/jnumed.111.096578
Braak, H., & Braak, E. (1995). Staging of Alzheimer’s disease-related neurofibrillary changes.
Neurobiology of Aging, 16(3), 271–284. https://doi.org/10.1016/0197-4580(95)00021- 6
Breijyeh, Z., & Karaman, R. (2020). Comprehensive Review on Alzheimer’s Disease: Causes and Treatment. Molecules (Basel, Switzerland), 25(24).
https://doi.org/10.3390/molecules25245789
Brier, M. R., Gordon, B., Friedrichsen, K., McCarthy, J., Stern, A., Christensen, J., … Ances, B. M. (2016). Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease.
Science Translational Medicine, 8(338), 338ra66.
https://doi.org/10.1126/scitranslmed.aaf2362
Burton, E. J., Barber, R., Mukaetova-Ladinska, E. B., Robson, J., Perry, R. H., Jaros, E., … O’Brien, J. T. (2009). Medial temporal lobe atrophy on MRI differentiates Alzheimer’s disease from dementia with Lewy bodies and vascular cognitive impairment: a
prospective study with pathological verification of diagnosis. Brain : A Journal of Neurology, 132(Pt 1), 195–203. https://doi.org/10.1093/brain/awn298
Byun, M. S., Kim, S. E., Park, J., Yi, D., Choe, Y. M., Sohn, B. K., … Initiative, A. D. N. (2015).
Heterogeneity of Regional Brain Atrophy Patterns Associated with Distinct Progression Rates in Alzheimer’s Disease. PloS One, 10(11), e0142756–e0142756.
https://doi.org/10.1371/journal.pone.0142756
Chételat, G. (2011). [Neuroimaging Alzheimer’s disease: early diagnosis, monitoring, and mechanism understanding]. Medecine sciences : M/S, 27(2), 193–198.
https://doi.org/10.1051/medsci/2011272193
Cho, H., Baek, M. S., Lee, H. S., Lee, J. H., Ryu, Y. H., & Lyoo, C. H. (2020). Principal
components of tau positron emission tomography and longitudinal tau accumulation in Alzheimer’s disease. Alzheimer’s Research & Therapy, 12(1), 114.
https://doi.org/10.1186/s13195-020-00685-4
Cho, H., Choi, J. Y., Hwang, M. S., Kim, Y. J., Lee, H. M., Lee, H. S., … Lyoo, C. H. (2016). In vivo cortical spreading pattern of tau and amyloid in the Alzheimer disease spectrum.
Annals of Neurology, 80(2), 247–258. https://doi.org/10.1002/ana.24711 Cho, H., Choi, J. Y., Lee, H. S., Lee, J. H., Ryu, Y. H., Lee, M. S., … Lyoo, C. H. (2019).
Progressive Tau Accumulation in Alzheimer Disease: 2-Year Follow-up Study. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine, 60(11), 1611–
1621. https://doi.org/10.2967/jnumed.118.221697
de Leon, M. J., Convit, A., Wolf, O. T., Tarshish, C. Y., DeSanti, S., Rusinek, H., … Fowler, J.
(2001). Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro- 2-deoxy-D-glucose/poitron-emission tomography (FDG/PET). Proceedings of the National Academy of Sciences of the United States of America, 98(19), 10966–10971.
https://doi.org/10.1073/pnas.191044198
Fan, L., Mao, C., Hu, X., Zhang, S., Yang, Z., Hu, Z., … Xu, Y. (2019). New Insights Into the Pathogenesis of Alzheimer’s Disease. Frontiers in Neurology, 10, 1312.
https://doi.org/10.3389/fneur.2019.01312
Fouquet, M., Desgranges, B., Landeau, B., Duchesnay, E., Mézenge, F., de la Sayette, V., … Chételat, G. (2009). Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer’s disease. Brain : A Journal of Neurology, 132(Pt 8), 2058–
2067. https://doi.org/10.1093/brain/awp132
Frisoni, G. B., Fox, N. C., Jack Jr, C. R., Scheltens, P., & Thompson, P. M. (2010). The clinical use of structural MRI in Alzheimer disease. Nature Reviews. Neurology, 6(2), 67–77.
https://doi.org/10.1038/nrneurol.2009.215
Giannakopoulos, P., Herrmann, F. R., Bussière, T., Bouras, C., Kövari, E., Perl, D. P., … Hof, P. R. (2003). Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology, 60(9), 1495–1500.
https://doi.org/10.1212/01.wnl.0000063311.58879.01
Gibson, G. E. (2002). Interactions of oxidative stress with cellular calcium dynamics and glucose metabolism in Alzheimer’s disease1, 2 1Guest Editors: Mark A. Smith and George Perry 2This article is a part of a series of reviews on “Causes and Consequences of Oxidative Stress. Free Radical Biology and Medicine, 32(11), 1061–1070.
https://doi.org/https://doi.org/10.1016/S0891-5849(02)00802-X
Gordon, B. A., Blazey, T. M., Christensen, J., Dincer, A., Flores, S., Keefe, S., … Benzinger, T.
L. S. (2019). Tau PET in autosomal dominant Alzheimer’s disease: relationship with cognition, dementia and other biomarkers. Brain : A Journal of Neurology, 142(4), 1063–1076. https://doi.org/10.1093/brain/awz019
Gordon, B. A., Blazey, T. M., Su, Y., Hari-Raj, A., Dincer, A., Flores, S., … Benzinger, T. L. S.
(2018). Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. The Lancet.
Neurology, 17(3), 241–250. https://doi.org/10.1016/S1474-4422(18)30028-0
Hirai, K., Aliev, G., Nunomura, A., Fujioka, H., Russell, R. L., Atwood, C. S., … Smith, M. A.
(2001). Mitochondrial abnormalities in Alzheimer’s disease. The Journal of
Neuroscience : The Official Journal of the Society for Neuroscience, 21(9), 3017–3023.
https://doi.org/10.1523/JNEUROSCI.21-09-03017.2001
Hunt, A., Schönknecht, P., Henze, M., Seidl, U., Haberkorn, U., & Schröder, J. (2007).
Reduced cerebral glucose metabolism in patients at risk for Alzheimer’s disease.
Psychiatry Research, 155(2), 147–154.
https://doi.org/10.1016/j.pscychresns.2006.12.003
Jack Jr, C R, Dickson, D. W., Parisi, J. E., Xu, Y. C., Cha, R. H., O’Brien, P. C., … Petersen, R.
C. (2002). Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology, 58(5), 750–757.
https://doi.org/10.1212/wnl.58.5.750
Jack Jr, Clifford R, Wiste, H. J., Vemuri, P., Weigand, S. D., Senjem, M. L., Zeng, G., … Initiative, the A. D. N. (2010). Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer’s disease. Brain, 133(11), 3336–3348. https://doi.org/10.1093/brain/awq277 Jack, C. R. J., Wiste, H. J., Weigand, S. D., Therneau, T. M., Lowe, V. J., Knopman, D. S., …
Petersen, R. C. (2017). Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association, 13(3), 205–216. https://doi.org/10.1016/j.jalz.2016.08.005
James, O. G., Doraiswamy, P. M., & Borges-Neto, S. (2015). PET Imaging of Tau Pathology in Alzheimer’s Disease and Tauopathies. Frontiers in Neurology, 6, 38.
https://doi.org/10.3389/fneur.2015.00038
Jie, C. V. M. L., Treyer, V., Schibli, R., & Mu, L. (2021). TauvidTM: The First FDA-Approved PET Tracer for Imaging Tau Pathology in Alzheimer’s Disease. Pharmaceuticals (Basel, Switzerland), 14(2). https://doi.org/10.3390/ph14020110
Johnson, K. A., Fox, N. C., Sperling, R. A., & Klunk, W. E. (2012). Brain imaging in Alzheimer disease. Cold Spring Harbor Perspectives in Medicine, 2(4), a006213.
https://doi.org/10.1101/cshperspect.a006213
Johnson, K. A., Schultz, A., Betensky, R. A., Becker, J. A., Sepulcre, J., Rentz, D., … Sperling, R. (2016). Tau positron emission tomographic imaging in aging and early Alzheimer disease. Annals of Neurology, 79(1), 110–119. https://doi.org/10.1002/ana.24546 Josephs, K. A., Whitwell, J. L., Weigand, S. D., Murray, M. E., Tosakulwong, N., Liesinger, A.
M., … Dickson, D. W. (2014). TDP-43 is a key player in the clinical features associated with Alzheimer’s disease. Acta Neuropathologica, 127(6), 811–824.
https://doi.org/10.1007/s00401-014-1269-z
Kljajevic, V., Grothe, M. J., Ewers, M., & Teipel, S. (2014). Distinct pattern of
hypometabolism and atrophy in preclinical and predementia Alzheimer’s disease.
Neurobiology of Aging, 35(9), 1973–1981.
https://doi.org/10.1016/j.neurobiolaging.2014.04.006
Kumar, A., Sidhu, J., Goyal, A., & Tsao, J. W. (2021). Alzheimer Disease. Treasure Island (FL).
Landau, S. M., Harvey, D., Madison, C. M., Koeppe, R. A., Reiman, E. M., Foster, N. L., … Jagust, W. J. (2011). Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiology of Aging, 32(7), 1207–1218.
https://doi.org/10.1016/j.neurobiolaging.2009.07.002
Lauretti, E., Li, J.-G., Di Meco, A., & Praticò, D. (2017). Glucose deficit triggers tau pathology and synaptic dysfunction in a tauopathy mouse model. Translational Psychiatry, 7(1), e1020–e1020. https://doi.org/10.1038/tp.2016.296
Leuzy, A., Chiotis, K., Lemoine, L., Gillberg, P.-G., Almkvist, O., Rodriguez-Vieitez, E., &
Nordberg, A. (2019). Tau PET imaging in neurodegenerative tauopathies-still a challenge. Molecular Psychiatry, 24(8), 1112–1134. https://doi.org/10.1038/s41380- 018-0342-8
Mocanu, M.-M., Nissen, A., Eckermann, K., Khlistunova, I., Biernat, J., Drexler, D., … Mandelkow, E.-M. (2008). The potential for beta-structure in the repeat domain of tau protein determines aggregation, synaptic decay, neuronal loss, and coassembly with endogenous Tau in inducible mouse models of tauopathy. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 28(3), 737–748.
https://doi.org/10.1523/JNEUROSCI.2824-07.2008
Mosconi, L, Mistur, R., Switalski, R., Brys, M., Glodzik, L., Rich, K., … de Leon, M. J. (2009).
Declining brain glucose metabolism in normal individuals with a maternal history of Alzheimer disease. Neurology, 72(6), 513–520.
https://doi.org/10.1212/01.wnl.0000333247.51383.43
Mosconi, Lisa. (2013). Glucose metabolism in normal aging and Alzheimer’s disease:
methodological and physiological considerations for PET studies. Clinical and Translational Imaging, 1(4), 217–233. https://doi.org/10.1007/s40336-013-0026-y
Mosconi, Lisa, Berti, V., Glodzik, L., Pupi, A., De Santi, S., & de Leon, M. J. (2010). Pre- clinical detection of Alzheimer’s disease using FDG-PET, with or without amyloid imaging. Journal of Alzheimer’s Disease : JAD, 20(3), 843–854.
https://doi.org/10.3233/JAD-2010-091504
Muralidar, S., Ambi, S. V., Sekaran, S., Thirumalai, D., & Palaniappan, B. (2020). Role of tau protein in Alzheimer’s disease: The prime pathological player. International Journal of Biological
Macromoleculesfile:///Users/Putuayuwidiaekaputri/Downloads/28241163.Nbib, 163, 1599–1617. https://doi.org/10.1016/j.ijbiomac.2020.07.327
Ou, Y.-N., Xu, W., Li, J.-Q., Guo, Y., Cui, M., Chen, K.-L., … Initiative, A. D. N. (2019). FDG- PET as an independent biomarker for Alzheimer’s biological diagnosis: a longitudinal study. Alzheimer’s Research & Therapy, 11(1), 57. https://doi.org/10.1186/s13195-019- 0512-1
Persson, K., Eldholm, R. S., Barca, M. L., Cavallin, L., Ferreira, D., Knapskog, A.-B., … Engedal, K. (2017). MRI-assessed atrophy subtypes in Alzheimer’s disease and the cognitive reserve hypothesis. PloS One, 12(10), e0186595.
https://doi.org/10.1371/journal.pone.0186595
Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., … Frisoni, G. B.
(2016). Brain atrophy in Alzheimer’s Disease and aging. Ageing Research Reviews, 30, 25–48. https://doi.org/10.1016/j.arr.2016.01.002
Rabinovici, G. D., Seeley, W. W., Kim, E. J., Gorno-Tempini, M. L., Rascovsky, K., Pagliaro, T. A., … Rosen, H. J. (2007). Distinct MRI atrophy patterns in autopsy-proven
Alzheimer’s disease and frontotemporal lobar degeneration. American Journal of Alzheimer’s Disease and Other Dementias, 22(6), 474–488.
https://doi.org/10.1177/1533317507308779
Rodriguez, G., Vitali, P., Calvini, P., Bordoni, C., Girtler, N., Taddei, G., … Nobili, F. (2000).
Hippocampal perfusion in mild Alzheimer’s disease. Psychiatry Research, 100(2), 65–
74. https://doi.org/10.1016/s0925-4927(00)00071-8
Schöll, M., Lockhart, S. N., Schonhaut, D. R., O’Neil, J. P., Janabi, M., Ossenkoppele, R., … Jagust, W. J. (2016). PET Imaging of Tau Deposition in the Aging Human Brain.
Neuron, 89(5), 971–982. https://doi.org/10.1016/j.neuron.2016.01.028
Schwarz, A. J., Sundell, K. L., Charil, A., Case, M. G., Jaeger, R. K., Scott, D., … Siemers, E. R.
(2019). Magnetic resonance imaging measures of brain atrophy from the EXPEDITION3 trial in mild Alzheimer’s disease. Alzheimer’s & Dementia (New York, N. Y.), 5, 328–
337. https://doi.org/10.1016/j.trci.2019.05.007
Shi, F., Liu, B., Zhou, Y., Yu, C., & Jiang, T. (2009). Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer’s disease: Meta-analyses of MRI studies.
Hippocampus, 19(11), 1055–1064. https://doi.org/10.1002/hipo.20573
Shivamurthy, V. K. N., Tahari, A. K., Marcus, C., & Subramaniam, R. M. (2015). Brain FDG PET and the diagnosis of dementia. AJR. American Journal of Roentgenology, 204(1), W76-85. https://doi.org/10.2214/AJR.13.12363
Smailagic, N., Vacante, M., Hyde, C., Martin, S., Ukoumunne, O., & Sachpekidis, C. (2015).
18F-FDG PET for the early diagnosis of Alzheimer’s disease dementia and other
dementias in people with mild cognitive impairment (MCI). The Cochrane Database of Systematic Reviews, 1(1), CD010632.
https://doi.org/10.1002/14651858.CD010632.pub2
Smith, R., Strandberg, O., Mattsson-Carlgren, N., Leuzy, A., Palmqvist, S., Pontecorvo, M. J.,
… Hansson, O. (2020). The accumulation rate of tau aggregates is higher in females and younger amyloid-positive subjects. Brain : A Journal of Neurology, 143(12), 3805–
3815. https://doi.org/10.1093/brain/awaa327
Smith, R., Wibom, M., Pawlik, D., Englund, E., & Hansson, O. (2019). Correlation of In Vivo [18F]Flortaucipir With Postmortem Alzheimer Disease Tau Pathology. JAMA
Neurology, 76(3), 310–317. https://doi.org/10.1001/jamaneurol.2018.3692
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., … Phelps, C.
H. (2011). Toward defining the preclinical stages of Alzheimer’s disease:
recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association, 7(3), 280–292.
https://doi.org/10.1016/j.jalz.2011.03.003
Thakur, A., Kamboj, P., Goswami, K., & Ahuja, K. (2018). Pathophysiology and management of alzheimer_s disease: an overview.
Tiwari, S., Atluri, V., Kaushik, A., Yndart, A., & Nair, M. (2019). Alzheimer’s disease:
pathogenesis, diagnostics, and therapeutics. International Journal of Nanomedicine, 14, 5541–5554. https://doi.org/10.2147/IJN.S200490
Vos, S. J. B., van Rossum, I. A., Verhey, F., Knol, D. L., Soininen, H., Wahlund, L.-O., … Visser, P. J. (2013). Prediction of Alzheimer disease in subjects with amnestic and nonamnestic MCI. Neurology, 80(12), 1124–1132.
https://doi.org/10.1212/WNL.0b013e318288690c
Wang, Y. T., & Edison, P. (2019). Tau Imaging in Neurodegenerative Diseases Using Positron Emission Tomography. Current Neurology and Neuroscience Reports, 19(7), 45.
https://doi.org/10.1007/s11910-019-0962-7
Whitwell, J. L., Dickson, D. W., Murray, M. E., Weigand, S. D., Tosakulwong, N., Senjem, M.
L., … Josephs, K. A. (2012). Neuroimaging correlates of pathologically defined subtypes of Alzheimer’s disease: a case-control study. The Lancet. Neurology, 11(10), 868–877.
https://doi.org/10.1016/S1474-4422(12)70200-4
Whitwell, J. L., Graff-Radford, J., Tosakulwong, N., Weigand, S. D., Machulda, M. M., Senjem, M. L., … Josephs, K. A. (2018). Imaging correlations of tau, amyloid, metabolism, and atrophy in typical and atypical Alzheimer’s disease. Alzheimer’s &
Dementia : The Journal of the Alzheimer’s Association, 14(8), 1005–1014.
https://doi.org/10.1016/j.jalz.2018.02.020
Xia, C., Makaretz, S. J., Caso, C., McGinnis, S., Gomperts, S. N., Sepulcre, J., … Dickerson, B.
C. (2017). Association of In Vivo [18F]AV-1451 Tau PET Imaging Results With Cortical Atrophy and Symptoms in Typical and Atypical Alzheimer Disease. JAMA Neurology, 74(4), 427–436. https://doi.org/10.1001/jamaneurol.2016.5755
Ye, B. S., Kim, H. J., Kim, Y. J., Jung, N.-Y., Lee, J. S., Lee, J., … Seo, S. W. (2018).
Longitudinal outcomes of amyloid positive versus negative amnestic mild cognitive impairments: a three-year longitudinal study. Scientific Reports, 8(1), 5557.
https://doi.org/10.1038/s41598-018-23676-w
Yiannopoulou, K. G., & Papageorgiou, S. G. (2020). Current and Future Treatments in Alzheimer Disease: An Update. Journal of Central Nervous System Disease, 12, 1179573520907397–1179573520907397. https://doi.org/10.1177/1179573520907397 Zhao, Q.-F., Tan, L., Wang, H.-F., Jiang, T., Tan, M.-S., Tan, L., … Yu, J.-T. (2016). The
prevalence of neuropsychiatric symptoms in Alzheimer’s disease: Systematic review and meta-analysis. Journal of Affective Disorders, 190, 264–271.
https://doi.org/10.1016/j.jad.2015.09.069
