Melanie Dani

Tau imaging in neurodegenerative diseases.

Aggregated tau protein is a major neuropathological substrate central to the pathophysiology of neurodegenerative diseases such as Alzheimer’s disease (AD), frontotemporal dementia, progressive supranuclear palsy, corticobasal degeneration and chronic traumatic encephalopathy. In AD, it has been shown that the density of hyperphosphorylated tau tangles correlates closely with neuronal dysfunction and cell death, unlike β-amyloid. Until now, diagnostic and pathologic information about tau deposition has only been available from invasive techniques such as brain biopsy or autopsy. The recent development of selective in-vivo tau PET imaging ligands including [18F]THK523, [18F]THK5117, [18F]THK5105 and [18F]THK5351, [18F]AV1451(T807) and [11C]PBB3 has provided information about the role of tau in the early phases of neurodegenerative diseases, and provided support for diagnosis, prognosis, and imaging biomarkers to track disease progression. Moreover, the spatial and longitudinal relationship of tau distribution compared with β - amyloid and other pathologies in these diseases can be mapped. In this review, we discuss the role of aggregated tau in tauopathies, the challenges posed in developing selective tau ligands as biomarkers, the state of development in tau tracers, and the new clinical information that has been uncovered, as well as the opportunities for improving diagnosis and designing clinical trials in the future.

Imaging biomarkers in tauopathies

Abnormally aggregated tau protein is central to the pathophysiology of Alzheimers disease, frontotemporal dementia variants, progressive supranuclear palsy, corticobasal degeneration and chronic traumatic encephalopathy. The post-mortem cortical density of hyperphosphorylated tau tangles correlates with pre-morbid cognitive dysfunction and neuron loss. Selective PET ligands including [18F]THK5117, [18F]THK5351, [18F]AV1451 (T807) and [11C]PBB3 now provide in vivo imaging information about the timing and distribution of tau in the early phases of neurodegenerative diseases. They are potential imaging biomarkers for both supporting diagnosis and tracking disease progression. Here, we discuss the challenges posed in developing selective tau ligands as biomarkers, their state of development and the new clinical information that has been revealed.

Suspected non-Alzheimer's pathology - Is it non-Alzheimer's or non-amyloid?

Neurodegeneration, the progressive loss of neurons, is a major process involved in dementia and age-related cognitive impairment. It can be detected clinically using currently available biomarker tests. Suspected Non-Alzheimer Pathology (SNAP) is a biomarker-based concept that encompasses a group of individuals with neurodegeneration, but no evidence of amyloid deposition (thereby distinguishing it from Alzheimers disease (AD)). These individuals may often have a clinical diagnosis of AD, but their clinical features, genetic susceptibility and progression can differ significantly, carrying crucial implications for precise diagnostics, clinical management, and efficacy of clinical drug trials. SNAP has caused wide interest in the dementia research community, because it is still unclear whether it represents distinct pathology separate from AD, or whether in some individuals, it could represent the earliest stage of AD. This debate has raised pertinent questions about the pathways to AD, the need for biomarkers, and the sensitivity of current biomarker tests. In this review, we discuss the biomarker and imaging trials that first recognized SNAP. We describe the pathological correlates of SNAP and comment on the different causes of neurodegeneration. Finally, we discuss the debate around the concept of SNAP, and further unanswered questions that are emerging.

Microglial activation correlates in vivo with both tau and amyloid in Alzheimer’s disease

Alzheimers disease is characterized by the histopathological presence of amyloid-β plaques and tau-containing neurofibrillary tangles. Microglial activation is also a recognized pathological component. The relationship between microglial activation and protein aggregation is still debated. We investigated the relationship between amyloid plaques, tau tangles and activated microglia using PET imaging. Fifty-one subjects (19 healthy controls, 16 mild cognitive impairment and 16 Alzheimers disease subjects) participated in the study. All subjects had neuropsychometric testing, MRI, amyloid (18F-flutemetamol), and microglial (11C-PBR28) PET. All subjects with mild cognitive impairment and Alzheimers disease and eight of the controls had tau (18F-AV1451) PET. 11C-PBR28 PET was analysed using Logan graphical analysis with an arterial plasma input function, while 18F-flutemetamol and 18F-AV1451 PET were analysed as target:cerebellar ratios to create parametric standardized uptake value ratio maps. Biological parametric mapping in the Statistical Parametric Mapping platform was used to examine correlations between uptake of tracers at a voxel-level. There were significant widespread clusters of positive correlation between levels of microglial activation and tau aggregation in both the mild cognitive impairment (amyloid-positive and amyloid-negative) and Alzheimers disease subjects. The correlations were stronger in Alzheimers disease than in mild cognitive impairment, suggesting that these pathologies increase together as disease progresses. Levels of microglial activation and amyloid deposition were also correlated, although in a different spatial distribution; correlations were stronger in mild cognitive impairment than Alzheimers subjects, in line with a plateauing of amyloid load with disease progression. Clusters of positive correlations between microglial activation and protein aggregation often targeted similar areas of association cortex, indicating that all three processes are present in specific vulnerable brain areas. For the first time using PET imaging, we show that microglial activation can correlate with both tau aggregation and amyloid deposition. This confirms the complex relationship between these processes. These results suggest that preventative treatment for Alzheimers disease should target all three processes.

STRATEGIES TO DEVELOP PARAMETRIC MAPS FOR TSPO PET TRACER [11C]-PBR28 IN PATIENTS WITH MILD COGNITIVE IMPAIRMENT

and DELCODE Consortium, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; University Hospital Cologne, Cologne, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurology, Otto von Guericke University, Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute for Stroke and Dementia Research (ISD), Klinikum der Universit€at M€unchen, Munich, Germany; German Center for Neurodegenerative Diseases, Munich, Germany; Department of Psychiatry and Psychotherapy, University Medical Center, Bonn, Germany; Department of Psychiatry, Charit e – Universit€atsmedizin Berlin, Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry, Charit e Universit€atsmedizin Berlin, Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center G€ottingen, G€ottingen, Germany; Department of Psychiatry and Psychotherapy, Eberhard Karls University, T€ubingen, Germany; German Center for Neurodegenerative Diseases (DZNE), T€ubingen, Germany; German Center for Neurodegenerative Disease (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany. Contact e-mail: wilma.bainbridge@nih.gov

MICROGLIAL ACTIVATION IS ASSOCIATED WITH HIGHER GREY MATTER DENSITY AND HIPPOCAMPAL VOLUME IN MCI SUBJECTS

Registration between MR images and histological sections allowed us to study critical parameters associated with plaques detection: size, compactness and iron load of plaques. Results: Histological evaluations revealed that patterns of amyloid plaques are similar in APPSL/PS1M146L, APP/PS1dE9 and human brains while they strikingly differ in other mice models. Compact amyloid plaques were always well detected by in vivo or ex vivo Gd-stained MRI unless they measured less than 40 mm. Diffuse plaques and intracellular amyloid deposits were never detected. Evaluation of iron depositions suggested that iron is important but not necessary for plaque detection by Gd-Stained MRI. Conclusions: We demonstrated that the ability to detect amyloid plaques with Gd-stained MR is strikingly different in the various mice models of amyloidosis or in humans. We also showed for the first time that Gd-stained MRI can detect amyloid plaques in post mortem human brains.

EVALUATION OF CASPASE-3 ACTIVATION IN AN ALZHEIMER’S DISEASE POPULATION USING [ 18 F]ICMT-11 PET/CT

Neuropathological studies have shown that tau accumulation is closely linked to cognitive decline. Using [F]AV1451 PET it is now possible to quantify tau pathology in vivo. The aim of this study was to investigate specific [F]AV1451 binding in relation to amyloid status and neuropsychological performance in cognitively normal subjects with SCD. Methods: 23 subjects with SCD (age 6667, 52% female, MMSE: 2861) underwent both 90 minute dynamic [F]florbetapir (amyloid) and 130 minute dynamic [F]AV1451 (tau) PET scans, and an extensive neuropsychological test battery. [F]Florbetapir scans were read visually as positive or negative for presence of cerebral amyloid. Reference parametric mapping (RPM) with cerebellar grey matter as reference region was used to calculate [F]AV1451 binding potential (BPND). Results were analysed for 1) medial temporal lobe (MTL), known as the first region to harbour tau pathology in aging, 2) a global region (neocortex), and 3) putamen, a region thought to represent “off-target” binding. Raw neuropsychological test scores were converted into z-scores and combined into composite scores for episodic memory. Results: [F]Florbetapir positive subjects (n1⁄47) showed higher [F]AV1451 BPND than [F]florbetapir negative subjects (n1⁄416) in MTL (0.2160.18 vs -0.0460.05, p<0.01) and neocortex (0.1460.06 vs 0.0260.04, p<0.01). Adjusted for age, sex and education, lower episodic memory scores were, although at trend level, associated with higher [F]AV1451 BPND in MTL (b1⁄4-0.52, p1⁄40.06) and neocortex (b1⁄4-0.49, p1⁄40.07), but not putamen (b1⁄4-0.25, p1⁄40.43). Across all subjects, regardless of amyloid status, higher age correlated with higher [F]AV1451 BPND in the putamen (Pearson’s r 1⁄4 0.66, p<0.01), but not with [F]AV1451 BPNDin MTL and neocortex. Conclusions:These preliminary data suggest that tau load in AD specific regions of patients with SCD is higher in amyloid positive than in amyloid negative subjects. In addition, tau accumulation in MTL and neocortex might be associated with poorer episodic memory performance. This highlights the potential of tau PET to capture early and subtle cognitive changes in subjects with SCD.

REGIONAL KINETIC MODELLING APPLICATION FOR TSPO PET TRACER [11C]PBR28

Background:Emerging evidences have demonstrated neuroinflammation has an important role in neurodegenerative disease. Second generation [11C]PBR28 PET has 80 fold affinity to TSPO compared to the previous tracers. Compared to kinetic models, the 3D parametric map enables simplified voxel-wise comparison between subjects or between regions. Here we evaluate for the first time the feasibility of generating [11C]PBR28 parametric map using spectral analysis, which may provide a better tracer binding differentiation as it account for the reversible and irreversible vascular component. We compared this with the Logan graphic analysis. Methods: 23 subjects (14MCI and 9HC) with TSPO high affinity binding were enrolled in this study. All subjects underwent T1/T2 weighted MRI and 90min [11C]PBR28 PET scans with arterial line. Spectral analysis was applied to evaluate the feasibility in generating various impulse response function (IRF) parametric maps at 1min, 30m, 45min, 60min, 75min and 90min. Graphic analysis was also applied to further evaluate the tracer signal and to compare with IRF parametric maps. Results:With arterial input function, spectrum identified three peaks: trapped tracer in plasma, and two tissue components. Among IRF(t) images with different time points, IRF(90min) parametric maps revealed the lowest noise. For the Logan graphic analysis, the [11C]PBR28 IRF(90min) parametric maps were generated using dynamic image for 6 data points (30w90mins), which showed a good correlation with Logan graphic analysis (r1⁄40.9285, p1⁄40.0001). Conclusions: In this study, for the first time, we have demonstrated that the spectral analysis could generate [11C]PBR28 parametric map, which showed consistency with Logan graphic analysis in frontal, temporal, parietal, occipital lobe, medial temporal lobe, hippocampus and thalamus. This demonstrate that the spectral analysis could be used to create [11C]PBR28 PET parametric maps which allows voxel-level comparison between regions and between subjects.

DOES NEUROINFLAMMATION PREDATE AMYLOID FORMATION IN SUBJECTS AT RISK FOR ALZHEIMER’S DISEASE?

gitudinal). [F]FDG intensity was normalized to the pons. Baseline global cortical [F]Florbetapir and mean parietal [F]FDG SUVR, as well as 2yr annualized percent change (APC) in global [F]Florbetapir were extracted. Baseline and 2-year hippocampal volumes (HV) were estimated using Freesurfer v5.1. Step-wise linear models predicting cortical or MTL [F]AV-1451 SUVR with the following predictors were assessed: cortical [F]Florbetapir SUVR, annual percent change (APC) in cortical [F]Florbetapir SUVR, mean parietal [F]FDG SUVR, HV, APC in HV, diagnosis (control or MCI/AD), age, gender, and time between scans. Results:Mean cortical [F]AV-1451 uptake was predicted only by diagnosis and cross-sectional mean cortical [F]Florbetapir SUVR. However, MTL [F]AV-1451 uptake was predicted by both mean cross-sectional cortical [F]Florbetapir uptake and cross-sectional HV. Conclusions: Overall, [F]AV-1451 uptake (representative of tau deposition) was predicted by the extent of cortical amyloid deposition ([F]Florbetapir uptake) approximately three years prior. Hippocampal atrophy may interact with this process. [1] Risacher et al. (2015) Alzheimer’s & Dementia, (11): 1417.