Tau and Pet/Mri Imaging Biomarkers for Detecting and Diagnosing Early Dementia

Research Article

Tau and Pet/Mri Imaging Biomarkers for Detecting and Diagnosing Early Dementia

Corresponding author:  Dr.Yongxia Zhou, 3740 McClintock Ave, Los Angeles, CA90089, Tel: 9179296684.
Email:Yongxia.zhou@yahoo.com

Abstract

Specific objective of this project is to quantify the neuropathological tau depositions in brain regions and to investigate primary age-related tau pathology and associations with amyloid and glucose-metabolism, neurocognitive tests and MRI metrics. Preliminary results demonstrated higher temporal deposition especially in the hippocampus and the inferior temporal regions compared to mean cortical deposition. Quantitative Braak stage-based regional analyses found highest tau deposition in the Braak stage II in the preclinical samples. There was significant age related higher tau deposition in temporal cortex, while non-significant correlation in mean cortical region. Significant correlations were found between tau deposition and memory as well as executive function scores, especially from middle and inferior temporal cortex; and significant correlations between tau and MRI metrics including diffusion, perfusion, functional and structural connectivities. Our results confirm current notion of this new tau tracer for reliably and consistently labeling and quantifying in vivo human taupathy in early dementia.

Keywords: Alzheimer’s Disease; Mild Cognitive Impairment; Tau; Amyloid; Fdg; Memory Score; Executive Function; Diffusion; Perfusion; Functional Connectivity; Morphometry

Introduction

As illustrated in the hypothetical model, compared to other PET tracers including beta-amyloid (Aβ), [18-F] Fluoro-2-deoxyglucose (FDG) and MRI structural/functional biomarkers, the intracellular tau deposition had been assumed to be particularly sensitive in the prodromal stage, especially the preclinical and mild cognitive impairment (MCI) stages [1,2]. The difficulties of labeling intracellular tau neurofibrillary tangle (NFT) deposition include requirement of high specificity of the tracer over the other five tau isoforms and enough abundance of the binding of the tracer to the intracellular tau[3,4]. The tracer molecular size needs to be small enough and designed to be lipophilic to cross the blood brain barrier (BBB) and cell membrane, to bind to the paired-helical filaments (PHF)-tau selectively[5]. With the development of tracer labeling and imaging techniques, over the past two decades, there are quite a few recent studies reported that successfully imaged tau depositions, especially the intracellular PHF which are insoluble fibers composed of hyper-phosphorylated tau, in MCI and Alzheimer’s disease (AD); and correlated with several cognitive tests [6,7]. However, due to recent availability of the PET tracer, there are very limited numbers of participants studied and most of them were relatively elderly [8-12].It has been recently reported that PET [18-F]AV-1451 tracer binds tau avidly to 3R + 4R tau in the form of NFT, and more specifically to AD type neurodegenerative disease than other types, especially in the early stages of dementia[13]. While most other tauopathies are characterized by the presence of both neuronal and glial tau pathologies [14]. Besides the PET imaging findings at molecular-level, alterations of tau-mediated brain functions had been investigated with several MRI metrics [15]. These MRI multiparametric findings using rTg4510 mouse model of taupathy had observed changes in brain atrophy (remarkable), cerebral blood flow (elevation during intervention), diffusion tensor imaging (DTI, downstream or later stage from formation of tau lesions) and amide proton transfer (decreased amide proton transfer-APT metabolism)[16].

We have recently investigated multiple MRI and conventional PET imaging biomarkers (amyloid and FDG) for differentiating early AD from normal controls and MCI; including structural and functional connectivity based on MRI (fcMRI), small-worldness analysis with MRI cortical thickness metric to better differentiate several stages of early dementia, and between MRI metrics such as white matter lesion load (a marker for cerebrovascular risk), functional activity and PET amyloid load for better understanding and defining the underlying neuropathological changes of brain circuits and association with phenotypic data [17-21]. Due to the relatively new development of the tau tracer, In vivo associations between PET tau and MRI metrics have not been reported and confirmed in human subjects yet. The between-modality interactions including human brain structural and functional networks undergo robust changes in normal aging and preclinical stages of AD, suggesting that neuroimaging metrics variability and integration from various networks in different scales might be useful to monitor brain changes[19].

Taken together, tau and amyloid are pathological hallmarks of AD. Emerging imaging techniques recently have enabled visualizing intracellular tau NFT in human brains in vivo with PET tracers. It remains unclear whether tau tangle deposition would cause dementia directly. After reviewing the current status of the imaging biomarkers for diagnosing and detecting MCI patients[22], the objective of this section is to present some preliminary results of the state-of-the-art imaging biomarkers including tau, Aβ, FDG and their correlations with neuropathological tests as well as multiparametric MRI findings based on the data from Alzheimer’s disease neuroimaging initiative (ADNI, http://adni.loni.usc.edu).

Materials and Methods

Participants

ADNI

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). For instance, MCI was based on the Petersen criteria and diagnosed when (1) cognitive impairment was evident for a single domain (typically short-term memory) or (2) cognitive impairment in multiple domains occurred without significant functional loss in activities of daily living.

Imaging Data

45 elderly individuals (mean ± std of age: 76.4 ± 7.3 years, range: 62.0-92.0 years; 18 women and 27 men) with [18-F] AV- 1451 PET ligand for imaging hyper-phosphorylated tau (3R + 4R), were downloaded from ADNI website (www.adni.loni.usc. edu) scanned during the period of 2015-May 2016. Amyloid imaging with [18-F]-Florbetapir (AV-45) PET ligand and FDG PET images for the available subjects that have both tau and amyloid or both tau and FDG data were downloaded as well. Subgroups included 18 normal controls (74.6 ± 6.2 years, 11 women/7 men), 24 MCI patients (77.0 ± 7.8 years, 5 women/ 19 men) and 3 patients with early AD (82.3 ± 8.6 years, 1 women/2 men).

The neuropsychological tests including two composite scores of executive function and memory domains were downloaded from the ADNI as well. In order to correlate with MRI imaging modalities, processed regional cerebral blood flow (CBF) (mean, minimal and maximum CBF values), diffusion tensor imaging (DTI) including fractional anisotropy (FA) and diffusivity, and functional connectivity of MRI (fcMRI) of default mode network (DMN) including ratio of voxel-to-ROI (RVR) metrics were downloaded as well. Other MRI data included structural T1-weighted magnetization-prepared rapid gradient- echo (MPRAGE), DTI and resting-state fMRI (RS-fMRI) data, as well as T2-weighted axial fluid attenuated inversion recovery (FLAIR) images.

Imaging Analysis

Both [18-F] AV-1451 and [18-F] AV-45, as well as [18-F] FDG PET images were analyzed to derive the ratio of standard uptake value (SUVR) choosing cerebellum as the tissue reference with in-house developed scripts. Namely, after normalization to the standard Montreal Neurologic Institute (MNI) space with the FMRIB Software Library (FSL, http://fsl.fmrib.ox.ac.uk/fsl), a well-validated atlas of total 116 regions of interest covering cortical and subcortical regions was used as regional parcellation.
Braak stage ROIs were derived from the atlas definitions by[6]. Statistical Pearson correlation between imaging metrics such as tau deposition and FDG deposition, as well as between imaging metrics and age, correlations with neuropsychological tests were implemented in MCI patients using FSL and MATLAB software (www.mathematics.com).

Voxel based morphometry (VBM) was implemented to the structural MRI data to detect regional concentration of gray matter atrophy using FSL toolbox. Voxel-wise tract-based spatial statistics (TBSS) in FSL was used to analyze DTI fractional anisotropy (FA) data. 3D correlation test of each voxel across subjects was performed using the Analysis of Functional NeuroImages (AFNI, http://afni.nimh.nih.gov/afni) to derive the correlation coefficient Rho for each voxel between PET and MRI metrics. Based on RS-fMRI, voxel-wise fractional amplitude of low frequency fluctuation (fALFF) technique and DMN fcMRI were obtained with adapted scripts in FSL (http://fsl. fmrib.ox.ac.uk/fsl, version 4.1.2) to detect neuronal activity at resting state[19].

Results

Tau Deposition Pattern and primary age-related taupathy (PART)

From group-averaged tau images, higher temporal deposition especially in the hippocampus, the inferior temporal and subcortical putamen/palladium compared to mean cortical and cerebellum deposition can be appreciated (Figure 1).

Figure 1. Higher temporal deposition especially in the hippocampus, the inferior temporal, and subcortical putamen/palladium (considered “off-target” binding) compared to mean cortical deposition and cerebellum (SUVR = 1) from group-averaged tau image.

Figure 2a illustrated the brain regions clustered based on Braak stages from literature [23]; and Figure 2b demonstrated across-group tau deposition with SUVR from ADNI data based on Braak stage ROIs as in a. Consistent with normal and early preclinical stages of individuals studied, Braak stage ROI quantification of group mean showed highest tau depositions at Braak stage II (significantly higher than other stages, P<0.05), lower in stages III/IV/V, and lowest in stages I/VI (both significantly lower than the other four stages, P<0.05) (Figure 2).

Quantitatively, significantly higher hippocampal tau retention compared to mean cortical regions was found (mean 1.37 ± 0.19 in hippocampus vs. 1.23 ± 0.16 in mean cortical regions; P = 0.006) (Figure 3a). Age related increases of tau deposition for several brain regions were found including hippocampus, temporal cortex and putamen (r = 0.46-0.48, P ≤ 0.02), while there was non-significant correlation with age in mean cortical region (P = 0.1) (Figure 3b).

Correlation between Tau and Neurocognitive tests

There were significant correlations between tau deposition and memory score in MCI patients. Most significant correlations (P < 0.01) in MCI group between tau deposition and neurological memory test (N = 24) from ADNI data are shown in Figure 4. The most significant negative correlations (P < 0.01) included: 1. tau in the left anterior division of the middle temporal gyrus and memory score (r = -0.5689, P = 0.0037); 2. tau in the left posterior division of the middle temporal gyrus and memory score (r = -0.5662, P = 0.0039); 3. tau in the left posterior division of the inferior temporal gyrus and memory score (r = -0.5342, P = 0.0072); and 4. tau in the left anterior division of the temporal fusiform cortex and memory score (r = -0.5178, P = 0.0095).

There were also significant correlations between tau deposition and executive function composite score in all samples (P<0.01, N=45), including: 1. tau in the left superior frontal gyrus and executive function score (r=-0.4075, P= 0.0055); 2. tau in the left middle frontal gyrus and executive function
Figure 2. a Brain regions clustered based on Braak staging from literature (Wang et al., 2016). b: Across-group tau deposition with SUVR based on Braak stage ROIs as in a., consistent with normal and early preclinical stages of individuals studied. Braak stage ROI quantification of group mean showed highest tau depositions at Braak stage II, lower in stages III/IV/V, and lowest in stages I/VI.
Figure 3. a: Significantly higher bilateral hippocampal tau retention compared to mean cortical regions (mean 1.37 ± 0.19 in hippocampus vs. 1.23 ± 0.16 in mean cortical regions; P = 0.006). b: Age related increases of tau deposition (r = 0.46-0.48, P = 0.02) for several brain regions including hippocampus, temporal cortex and putamen, while non-significant correlation with age in mean cortical region (P = 0.1).

Figure 4. Significant correlations (corrected P < 0.01) between tau deposition and composite memory score in MCI patients. Negative correlations are shown in cold blue to cyan color; and positive correlations are shown in red-yellow color (only in the entorhinal cortex, P≤0.05).

score (r=-0.3953, P= 0.0072); and 3. tau in the right amygdala and executive function score (r=-0.4819, P= 0.0008). Especially in normal elderly sub-samples, strong associations were found between tau in the temporal cortex and executive function score (P<0.01, N=18); including: 1. tau in the left anterior division of the middle temporal gyrus and executive function
score (r=-0.6205, P= 0.006); 2. tau in the left anterior division of the parahippocampal gyrus and executive function score (r=-0.5925, P= 0.0096); 3. tau in the right anterior division of the parahippocampal gyrus and executive function score (r=- 0.6440, P= 0.0039); and finally tau in the right amygdala and executive function score (r=-0.5907, P= 0.0098).

Correlations between Tau and amyloid
In addition, we also observed significant correlations between tau and amyloid deposition in MCI patients. The most significant correlations (P ≤ 0.001) in MCI group between tau and Aβ depositions (N = 18) from ADNI data is shown in Figure 5.
Figure 5. Significant correlations (P < 0.001) between tau deposition and amyloid deposition in MCI patients. Negative correlations are shown in cold blue to cyan color, and positive correlations in red-yellow color
Significant positive correlations included: 1. tau in the left pars triangularis portion of the inferior frontal gyrus and Aβ in theleft postcentral gyrus (r = 0.7164, P = 0.0008); 2. tau in the left pars triangularis portion of the inferior frontal gyrus and Aβ in the right anterior division portion of the supramarginal gyrus (r = 0.7586, P = 0.0003); 3. tau in the left pars triangularisportion of the inferior frontal gyrus and Aβ in the right occipital pole (r = 0.7412, P = 0.0004); 4. tau and Aβ in the rightanterior division of the parahippocampal gyrus (r = 0.4260, P = 0.0169); and 5. tau and Aβ in the right occipital pole (r =0.3958, P = 0.0275). Significant negative correlations included: 1. tau and Aβ in the anterior division of the right superior temporal gyrus (r = -0.3742, P = 0.0381); 2. tau and Aβ in the right frontal medial cortex (r = -0.4129, P = 0.021); 3. tau and Aβ in the right parietal operculum cortex (r = -0.3739, P = 0.0383); 4. tau in the left planum temporale and Aβ in the left occipital fusiform gyrus (r = -0.7317, P = 0.0006); 5. tau in the left temporal occipital fusiform cortex and Aβ in the right parietal operculum cortex (r = -0.7110, P = 0.0009); and 6. tau in the right subcallosal cortex and Aβ in the left parietal operculum cortex (r = -0.7088, P = 0.001).
Correlations between Tau and FDG
The most significant negative correlations (P ≤ 0.001) in MCI group between tau deposition and FDG depositions (N = 15)from ADNI data include: 1. tau in the right intracalcarine cortex and FDG in the left posterior division of the inferior temporalgyrus (r = -0.7611, P = 0.0010); 2. tau in the left intracalcarine cortex and FDG in the left central opercular cortex (r =-0.7649, P = 0.0009); 3. tau in the left pallidum and FDG in the left amygdala (r = -0.7621, P = 0.0010); 4. tau in the left caudate and FDG in the anterior division of the right inferior temporal gyrus (r = -0.8052, P = 0.0003); 5. tau in the left caudate and FDG in the right anterior division of the temporal fusiform cortex (r = -0.7801, P = 0.0006). And positive correlations include: 1. tau in the anterior division of the left supramarginal  gyrus and FDG in the left brainstem (r = 0.7914, P = 0.0004); 2.tau in the left frontal pole and FDG in the right occipital pole (r = 0.8093, P = 0.0003).
Correlation between Tau and multiparametric MRI
In addition, we also found significant correlations between regional tau and MRI parameters including DTI and perfusionmetrics, in even small available samples. A representative subject with multi-modality imaging including PET tau, Aβ, FDG and MRI FLAIR, DTI and MPRAGE is demonstrated in Figure 6.
Figure 6. A representative subject with multi-modality imaging including PET FDG (a), Aβ (b), Tau (c) and MRI T2-FLAIR (d), DTI (e) and MPRAGE (f) is demonstrated. Higher FDG glucose metabolism in the default mode network region including posterior cingulate, medial prefrontal cortex, cortical gray matter and subcortical regions (a); relatively higher deposition of amyloid in the posterior cingulate (b) and higher deposition of tau in the middle and inferior temporal lobe, posterior cingulate and bilateral parietal regions (c) can be appreciated.
Peri-ventricular white matter hyperintensity lesions (d), ventricular enlargement (e) and mild cortical atrophy (f) on MRI images were observed as well.
Higher FDG glucose metabolism in the default mode network region including posterior cingulate and medial prefrontal cortex, cortical gray matter and subcortical regions (a), relatively higher deposition of amyloid in the poster cingulate (b) and higher deposition of tau in the middle and inferior temporal lobe, poster cingulate and bilateral parietal regions (c) can be appreciated. On the MRI images for the same subject, peri-ventricular white matter hyperintensity lesions (d), ventricular enlargement (e) and mild cortical atrophy (f) were observed as well.
Regarding cross-modality associations in MCI patients, based on ROI analysis, highly significant correlations were found between regional tau deposition and MRI metrics (P<0.01), as listed: 1. Strong associations were found between frontal tau deposition and DTI FA value of the hippocampal portion of the cingulum bundle (r = -0.9918, P = 0.0082); as well as between middle frontal tau deposition and FA value of the genu of the corpus callosum (r = -0.9994, P = 0.0006). 2. Significant correlations existed between tau deposition and cerebral blood flow (CBF) measured with MRI arterial spin labeling (ASL) technique. For instance, in MCI group, significant correlation between middle temporal tau and mean CBF value in the lateral- orbito-frontal region (r = 0.8862, P = 0.0006) was found. 3.

 

Figure 7. a: Significant voxel-wise negative correlation between tau and GM density were found in the entorhinal and hippocampal regions, medial and orbito-frontal as well as cerebellum; with few positive correlations found in scattered brain regions including superior frontal cortex (corrected P<0.05). b: Significant voxel-wise negative correlations between tau and DTI FA were found in the hippocampal portion of the cingulum bundle, thalamic radiation, inferior longitudinal fasciculus (corrected P<0.05) together with some positive correlations found in frontal white matter. Other significant PET tau and MRI correlations included right hippocampal tau and functional connectivity of the DMN RVR (r = 0.8816, P = 0.0087).
Based on in-house voxel-wise correlation analyses, significant voxel-wise negative correlation between tau and GM densitywere found in the entorhinal and hippocampal regions, medial and orbito-frontal as well as cerebellum; with few positivecorrelations found in scattered brain regions including superior frontal cortex (corrected P<0.05, Figure 7b). Significant voxel-wise negative correlations between tau and DTI FA were found in the hippocampal portion of the cingulum bundle, thalamic radiation, inferior longitudinal fasciculus (corrected P<0.05) together with some positive correlations found in frontal white matter (Figure 7a). On the other hand, there were no significant voxel-wise correlations (P>0.05) between tau and fALFF, as well as between tau and DMN connectivity strength.
Discussion
Tau images demonstrated expected higher deposition in the early possible pathological regions in hippocampal and temporalcortices. As expected and validated, quantitative Braak stage-based regional analyses showed highest tau deposition in the Braak stage II in the preclinical samples. Significant correlations were also found between tau and multiple MRI metricsincluding diffusion, perfusion and functional connectivity. The concurrence of GM atrophy, structural disconnectivity andfunctional hyper-perfusion and the higher tau in hippocampal and other temporal regions suggested a possible structuralchange through local diffusion together with tau spreading and a probable functional compensation through trans-synapticfunctional connectivity and perfusion in MCI patients. Consistent with reported results, we observed significant PART effectincluding hippocampus and temporal cortex. Furthermore, strong correlations were found between tau deposition in differentsub-segments of temporal cortex and composite neurocognitive scores in the domain of memory and executive function from the ADNI data, as well as between regional tau and amyloid (mostly cortical regions), and between tau and FDG (including some sub-cortical regions). Our findings confirmed the validity of the new tau tracer for labeling intracellular tau deposition, and indicated the important neuropathological and neurocognitive roles of this new tau tracer as a component in the profile of multiple brain imaging biomarkers at preclinical dementia stage.
The observation of regional tau deposition pattern and neuropathological staging based on Braak ROIs are consistent withrecent findings [24-26]. The PART effects observed in bilateral hippocampus and temporal cortices but not in other corticalregions confirm with recent tau pathological findings that PART-type pathology generally does not progress to the isocorticalBraak stages and has limited extension beyond the temporal neocortex to other neocortical regions [27]. The quantitative SUV values were consistent with reported values in preclinical elderly samples, for instance, mean SUV=1.18-1.19 for clinically normal individuals [28]. We also observed expected regional tau distribution pattern and correlations with amyloid and FDG tracers. A recent study had reported voxel-wise focal negative correlations between [18-F] THK5317 retention and [18-F]FDG uptake, mainly in the frontal cortex, and focal positive correlations were found between [18-F]THK5317 and [11-C]PIB retentions isocortically [8].
Although we only used static [18-F]-AV-1451 SUV values, recent kinetic analysis based on [18-F]-AV-1451 SUVR curves suggesting that an SUVR calculated over imaging window of 80-100 min (as currently used in clinical studies) provides estimates of PHF tau burden in good correlation with non-specific binding potential of tau[29]. Our correlational result between PET tracers and memory tests was consistent with recent findings that tau was associated with decline in global cognition [25].We also found significant correlations between tau and executive function composite score in our preclinical samples includingfrontal and amygdale regions as well as strong associations between tau in temporal cortex and executive function composite score in normal individuals (all P<0.01). These results were consistent with the finding that worse performance on domain-specific neuropsychological tests was associated with greater [18-F]-AV1451 uptake in key regions implicated in memory (medial temporal lobes), visuospatial function and language[9].
The Aβ deposition is assumed to be the “trigger” of the tau to cause the neurodegeneration in brain based on its earlier onsetof pathological accumulation compared to the PHF-tau [2,28]. It has been suggested that tau spreading in other isocorticalregions required the presence of cortical Aβ [30,25]. Whether PHF-tau can serve as an independent tracer to diagnose MCIpatients (especially in the Aβ+ sub-samples) probably needs further investigation. One of the future directions would probablybe to construct disease-specific tau distribution patterns including not only early AD (which is spatially more confined to medial temporal lobe) but also other types of dementia such as fronto-temporal dementia [31,6]. Current development ofsimultaneous imaging of MRI and PET also offers the opportunity to image the neuropathological disease burden together with the high spatial-resolution structural and functional MRI information including structural-functional connectivities, functional perfusion and brain atrophy with the advantages of intrinsic between-modality imaging registration and less radiation and patient discomfort[32]. Further validation with more samples at various age ranges, correlation and test-retest with other imaging metrics and pathological data including cerebrospinal fluid tau and genetics to elucidate multiple factors contributing to the neurodegeneration processes in brain are warranted. A few unexpected correlations were found using voxel-wise and ROI analyses, and results might be improved with more samples. Potential future work also includes adjustment for these confounding factors including clinical and genetic data and acquisition of longitudinal data to elucidate the causality effects of different imaging biomarkers [33,11].
To improve the accuracy of the diagnosis of the tracer, reduction of the off-target binding of the tau tracers (e.g., subcorticalstriatal regions), and incorporating kinetic parametric results such as R1 and DVR similar to the amyloid imaging quantificationat different disease population might help to further the interpretation of the subcortical to cortical tau spreadingresults[29,8,34,23]. More samples with tau and amyloid imaging and stratifying the tau deposition with Aβ+ and Aβ- subgroups might help clarify the current notion that Aβ absence may be insufficient to cause neurodegeneration process thatlead to AD[35].
Conclusion

The imaging results from ADNI data based on our analyses demonstrated higher temporal tau deposition especially in thehippocampus and the inferior temporal regions compared to mean cortical deposition. There was also significant age relatedincrease of tau deposition for several specific brain regions including temporal cortex, while non-significant correlation with age in mean cortical region. We also found significant correlations between tau deposition and memory/executive function scores in MCI patients, especially from the middle and inferior temporal cortices; and significant correlations between tau and amyloid, tau and FDG; between tau and multiple MRI metrics. Our results confirm the current notion of this relatively new [18-F]AV-1451 tau tracer for in vivo labeling PHF tau deposition and the agreement with the tau-pathological Braak stage and other imaging metrics in early dementia.
Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (NationalInstitutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson  Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California.

References
  1. Ariza M, Kolb HC, Moechars D, Rombouts F, Andres JI. Tau Positron Emission Tomography (PET) Imaging: Past, Present, and Future. J Med Chem. 2015, 58(11):4365-4382.
  2. Barret O, Alagille D, Sanabria S, Comley RA, Weimer RM, Borroni E et al. Kinetic Modeling of the Tau PET Tracer 18F-AV-1451 in Human Healthy Volunteers and Alzheimer’s Disease Subjects. J Nucl Med. 2016.
  3. Brendel M, Jaworska A, Probst F, Overhoff F, Korzhova V, Lindner S et al. (2016) Small-Animal PET Imaging of Tau Pathology with 18F-THK5117 in 2 Transgenic Mouse Models. J Nucl Med. 2016, 57(5):792-798.
  4. Brier MR, Gordon B, Friedrichsen K, McCarthy J, Stern A, Christensen J et al. (2016) Tau and Abeta imaging, CSF measures, and cognition in Alzheimer’s disease. Sci Transl Med. 2016,11(8):338ra366.
  5. Chiotis K, Saint-Aubert L, Savitcheva I, Jelic V, Andersen P, Jonasson M et al. Imaging in-vivo tau pathology in Alzheimer’s disease with THK5317 PET in a multimodal paradigm. Eur J Nucl Med Mol Imaging. 2016, 43(9):1686-1699.
  6. Clavaguera F, Goedert M, Tolnay M. Induction and spreading of tau pathology in a mouse model of Alzheimer’s disease. Med Sci (Paris). 2010, 26:121-124.
  7. Holmes HE, Colgan N, Ismail O, Ma D, Powell NM, O’Callaghan JM et al. Imaging the accumulation and suppression of tau pathology using multiparametric MRI. Neurobiol Aging. 2016, 39:184-194.
  8. Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010, 9(1):119-128.
  9. Jack CR Jr, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013, (2):207-216.
  10. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016, 87(5):539-547.
  11. Jack CR Jr, Wiste HJ, Weigand SD, Therneau TM, Lowe VJ, Knopman DS et al. (2016b) Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimers Dement. 2017,13(3) :205-216.
  12. Kahlson MA, Colodner KJ. Glial Tau Pathology in Tauopathies: Functional Consequences. J Exp Neurosci. 2015, 9:43-50.
  13. Lim YY, Hassenstab J, Cruchaga C, Goate A, Fagan AM, Benzinger TL et al. BDNF Val66Met moderates memory impairment, hippocampal function and tau in preclinical autosomal dominant Alzheimer’s disease. Brain. 2016, 139(10):2766-2777.
  14. Lockhart SN, Baker SL, Okamura N, Furukawa K, Ishiki A, Furumoto S et al. Dynamic PET Measures of Tau Accumulation in Cognitively Normal Older Adults and Alzheimer’s Disease Patients Measured Using [18F] THK-5351. PLoS One. 2016, 11(6):e0158460.
  15. Lowe VJ, Curran G, Fang P, Liesinger AM, Josephs KA, Parisi JE et al. An autoradiographic evaluation of AV-1451 Tau PET in dementia. Acta Neuropathol Commun. 2016, 4(1):58.
  16. Ossenkoppele R, Schonhaut DR, Scholl M, Lockhart SN, Ayakta N, Baker SL et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain. 2016, 139(5):1551-1567.
  17. Prashanthi V LV, Knopman DS, Senjem ML, Kemp KJ, Schwarz CG, Przybelski et al. Tau-PET uptake: Regional variation in average SUVR and impact of amyloid deposition. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 2017, 6: 21-30.
  18. Rodriguez-Vieitez E, Saint-Aubert L, Carter SF, Almkvist O, Farid K, Scholl M et al. Diverging longitudinal changes in astrocytosis and amyloid PET in autosomal dominant Alzheimer’s disease. Brain. 2016, 139(3):922-936.
  19. Sarazin M, Lagarde J, Bottlaender M. Distinct tau PET imaging patterns in typical and atypical Alzheimer’s disease. Brain. 2016, 139:1321-1324.
  20. Scholl M, Lockhart SN, Schonhaut DR, O’Neil JP, Janabi M, Ossenkoppele R et al. (2016) PET Imaging of Tau Deposition in the Aging Human Brain. Neuron. 2016,89(5):971-982.
  21. Schwarz AJ, Yu P, Miller BB, Shcherbinin S, Dickson J, Navitsky M et al. Regional profiles of the candidate tau PET ligand 18F-AV-1451 recapitulate key features of Braak histopathological stages. Brain. 2016,139(5):1539-1550.
  22. Shcherbinin S, Schwarz AJ, Joshi A, Navitsky M, Flitter M, Shankle WR et al. Kinetics of the Tau PET Tracer 18F-AV-1451 (T807) in Subjects with Normal Cognitive Function, Mild Cognitive Impairment, and Alzheimer Disease. J Nucl Med. 2016, 57(10):1535-1542.
  23. Smith R, Puschmann A, Scholl M, Ohlsson T, van Swieten J, Honer M et al. 18F-AV-1451 tau PET imaging correlates strongly with tau neuropathology in MAPT mutation carriers. Brain. 2016, 139(9):2372-2379.
  24. Sperling R, Mormino E, Johnson K. The evolution of preclinical Alzheimer’s disease: implications for prevention trials.
  25. Villemagne VL, Okamura N. In vivo tau imaging: obstacles and progress. Alzheimers Dement. 2014, 10(3):S254-S264.
  26. Villemagne VL, Okamura N. Tau imaging in the study of ageing, Alzheimer’s disease, and other neurodegenerative conditions. Curr Opin Neurobiol. 2016, 36:43-51.
  27. Wang L, Benzinger TL, Su Y, Christensen J, Friedrichsen K, Aldea P et al. Evaluation of Tau Imaging in Staging Alzheimer Disease and Revealing Interactions Between beta-Amyloid and Tauopathy. JAMA Neurol. 2016, 73(9):1070-1077.
  28. Wells JA, O’Callaghan JM, Holmes HE, Powell NM, Johnson RA, Siow B et al. In vivo imaging of tau pathology using multi-parametric quantitative MRI. Neuroimage. 2015, 111:369-378.
  29. Xia C, Dickerson BC. Tau PET: the next frontier in molecular imaging of dementia. 2016,Int Psychogeriatr:1-4.
  30. Zhou Y, Sojkova J, Resnick SM, Wong DF. Relative equilibrium plot improves graphical analysis and allows bias correction of standardized uptake value ratio in quantitative 11C-PiB PET studies. J Nucl Med. 2012, 53(4):622-628.
  31. Zhou Y, Lui YW. Small-World Properties in Mild Cognitive Impairment and Early Alzheimer’s Disease: A Cortical Thickness MRI Study. ISRN Geriatr. 2013.
  32. Zhou Y, Yu F, Duong TQ, Alzheimer’s Disease Neuroimaging Initiative. White matter lesion load is associated with resting state functional MRI activity and amyloid PET but not FDG in mild cognitive impairment and early Alzheimer’s disease patients. J Magn Reson Imaging. 2015, 41(1):102-109.
  33. Zhou Y, Dougherty JH Jr, Hubner KF, Bai B, Cannon RL, Hutson RK. Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer’s disease and mild cognitive impairment. Alzheimers Dement. 2008, 4(4):265-270.
  34. Zhou Y (2016a) Functional Neuroimaging with Multiple Modalities: Nova Publishers.
  35. Zhou Y (2016b) Quantitative PET/MRI Evaluation and Application in Dementia. Jacobs J Med Diagn Med Imaging 1.

     36. Zhou Y (2017) Mild Cognitive Impairment (MCI): Diagnosis, Prevalence and Quality of Life, Chapter 1-Imaigng Dection and  Diagnosis of Mild   Cognitive Impairment: Promising Biomarkers at Different Stages of Early Dementia. Nova Science Publishers.

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