Tau and Pet/Mri Imaging Biomarkers for Detecting and Diagnosing Early Dementia
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.
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, 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
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.
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.
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. 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.
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 ; 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).
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).
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.
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.
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