Reproducibility of quantitative (R)-[11C]verapamil studies

Background P-glycoprotein [Pgp] dysfunction may be involved in neurodegenerative diseases, such as Alzheimer's disease, and in drug resistant epilepsy. Positron emission tomography using the Pgp substrate tracer (R)-[11C]verapamil enables in vivo quantification of Pgp function at the human blood-brain barrier. Knowledge of test-retest variability is important for assessing changes over time or after treatment with disease-modifying drugs. The purpose of this study was to assess reproducibility of several tracer kinetic models used for analysis of (R)-[11C]verapamil data. Methods Dynamic (R)-[11C]verapamil scans with arterial sampling were performed twice on the same day in 13 healthy controls. Data were reconstructed using both filtered back projection [FBP] and partial volume corrected ordered subset expectation maximization [PVC OSEM]. All data were analysed using single-tissue and two-tissue compartment models. Global and regional test-retest variability was determined for various outcome measures. Results Analysis using the Akaike information criterion showed that a constrained two-tissue compartment model provided the best fits to the data. Global test-retest variability of the volume of distribution was comparable for single-tissue (6%) and constrained two-tissue (9%) compartment models. Using a single-tissue compartment model covering the first 10 min of data yielded acceptable global test-retest variability (9%) for the outcome measure K1. Test-retest variability of binding potential derived from the constrained two-tissue compartment model was less robust, but still acceptable (22%). Test-retest variability was comparable for PVC OSEM and FBP reconstructed data. Conclusion The model of choice for analysing (R)-[11C]verapamil data is a constrained two-tissue compartment model

Spatial-Temporal Patterns of Amyloid-β Accumulation: A Subtype and Stage Inference Model Analysis

BACKGROUND AND OBJECTIVES: Currently, amyloid-β (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS: Amyloid-PET data of 3010 subjects were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios (SUVr) were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion (CVIC) and the most probable subtype/stage classification per scan. The effect of demographics and risk factors on subtype assignment was assessed using multinomial logistic regression. RESULTS: Participants were mostly cognitively unimpaired (N=1890, 62.8%), had a mean age of 68.72 (SD=9.1), 42.1% was APOE-ε4 carrier, and 51.8% was female. While a one-subtype model recovered the traditional amyloid accumulation trajectory, SuStaIn identified an optimal of three subtypes, referred to as Frontal, Parietal, and Occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to Frontal (N=415, 52.5%), followed by Parietal (N=199, 25.3%), and Occipital subtypes (N=175, 22.2%). Significant differences across subtypes included distinct proportions of APOE-ε4 carriers (Frontal:61.8%, Parietal:57.1%, Occipital:49.4%), subjects with dementia (Frontal:19.7%, Parietal:19.1%, Occipital:31.0%) and lower age for the Parietal subtype (Frontal/Occipital:72.1y, Parietal:69.3y). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the Frontal subtype, while Parietal and Occipital did not differ. At follow-up, most subjects (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION: While a one-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that three subtypes were optimal, showing distinct associations to AD risk factors. Nonetheless, further analyses to determine clinical utility is warranted

Kinetics and 28-day test-retest repeatability and reproducibility of [C-11]UCB-J PET brain imaging

[C-11]UCB-J is a novel radioligand that binds to synaptic vesicle glycoprotein 2A (SV2A). The main objective of this study was to determine the 28-day test-retest repeatability (TRT) of quantitative [C-11]UCB-J brain positron emission tomography (PET) imaging in Alzheimer's disease (AD) patients and healthy controls (HCs). Nine HCs and eight AD patients underwent two 60 min dynamic [C-11]UCB-J PET scans with arterial sampling with an interval of 28 days. The optimal tracer kinetic model was assessed using the Akaike criteria (AIC). Micro-/macro-parameters such as tracer delivery (K-1) and volume of distribution (V-T) were estimated using the optimal model. Data were also analysed for simplified reference tissue model (SRTM) with centrum semi-ovale (white matter) as reference region. Based on AIC, both 1T2k_V-B and 2T4k_V-B described the [C-11]UCB-J kinetics equally well. Analysis showed that whole-brain grey matter TRT for V-T, DVR and SRTM BPND were -2.2% +/- 8.5, 0.4% +/- 12.0 and -8.0% +/- 10.2, averaged over all subjects. [C-11]UCB-J kinetics can be well described by a 1T2k_V-B model, and a 60 min scan duration was sufficient to obtain reliable estimates for both plasma input and reference tissue models. TRT for V-T, DVR and BPND wa

Genetically identical twin-pair difference models support the amyloid cascade hypothesis

The amyloid cascade hypothesis has strongly impacted the Alzheimer's disease research agenda and clinical trial designs over the past decades, but precisely how amyloid-β pathology initiates the aggregation of neocortical tau remains unclear. We cannot exclude the possibility of a shared upstream process driving both amyloid-β and tau in an independent manner instead of there being a causal relationship between amyloid-β and tau. Here, we tested the premise that if a causal relationship exists, then exposure should be associated with outcome both at the individual-level as well as within identical twin-pairs, who are strongly matched on genetic, demographic and shared environmental background. Specifically, we tested associations between longitudinal amyloid-β PET and cross-sectional tau-PET, neurodegeneration and cognitive decline using genetically identical twin-pair difference models, which provide the unique opportunity of ruling out genetic and shared environmental effects as potential confounders in an association. We included 78 cognitively unimpaired identical twins with [18F]flutemetamol (amyloid-β)-PET, [18F]flortaucipir (tau)-PET, MRI (hippocampal volume), and cognitive data (composite memory). Associations between each modality were tested at the individual-level using generalized estimating equation models, and within identical twin-pairs using within-pair difference models. Mediation analyses were performed to test for directionality in the associations as suggested by the amyloid cascade hypothesis. At the individual-level, we observed moderate-to-strong associations between amyloid-β, tau, neurodegeneration and cognition. The within-pair difference models replicated results observed at the individual-level with comparably strong effect sizes. Within-pair differences in amyloid-β were strongly associated with within-pair differences in tau (β=0.68, p < 0.001), and moderately associated with within-pair differences in hippocampal volume (β=-0.37, p = 0.03) and memory functioning (β=-0.57, p < 0.001). Within-pair differences in tau were moderately associated with within-pair differences in hippocampal volume (β=-0.53, p < 0.001) and strongly associated with within-pair differences in memory functioning (β=-0.68, p < 0.001). Mediation analyses showed that of the total twin-difference effect of amyloid-β on memory functioning, the proportion mediated through pathways including tau and hippocampal volume was 69.9%, which was largely attributable to the pathway leading from amyloid-β to tau to memory functioning (proportion mediated: 51.6%). Our results indicate that associations between amyloid-β, tau, neurodegeneration and cognition are unbiased by (genetic) confounding. Furthermore, effects of amyloid-β on neurodegeneration and cognitive decline were fully mediated by tau. These novel findings in this unique sample of identical twins are compatible with the amyloid cascade hypothesis and thereby provide important new knowledge for clinical trial designs

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio

Gray Matter Changes in Parkinson's and Alzheimer's Disease and Relation to Cognition

Purpose of Review We summarize structural (s)MRI findings of gray matter (GM) atrophy related to cognitive impairment in Alzheimer's disease (AD) and Parkinson's disease (PD) in light of new analytical approaches and recent longitudinal studies results. Recent Findings The hippocampus-to-cortex ratio seems to be the best sMRI biomarker to discriminate between various AD subtypes, following the spatial distribution of tau pathology, and predict rate of cognitive decline. PD is clinically far more variable than AD, with heterogeneous underlying brain pathology. Novel multivariate approaches have been used to describe patterns of early subcortical and cortical changes that relate to more malignant courses of PD. New emerging analytical approaches that combine structural MRI data with clinical and other biomarker outcomes hold promise for detecting specific GM changes in the early stages of PD and preclinical AD that may predict mild cognitive impairment and dementia conversion

Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis

IMPORTANCE: Amyloid-β positron emission tomography (PET) imaging allows in vivo detection of fibrillar plaques, a core neuropathological feature of Alzheimer disease (AD). Its diagnostic utility is still unclear because amyloid plaques also occur in patients with non-AD dementia. OBJECTIVE: To use individual participant data meta-analysis to estimate the prevalence of amyloid positivity on PET in a wide variety of dementia syndromes. DATA SOURCES: The MEDLINE and Web of Science databases were searched from January 2004 to April 2015 for amyloid PET studies. STUDY SELECTION: Case reports and studies on neurological or psychiatric diseases other than dementia were excluded. Corresponding authors of eligible cohorts were invited to provide individual participant data. DATA EXTRACTION AND SYNTHESIS: Data were provided for 1359 participants with clinically diagnosed AD and 538 participants with non-AD dementia. The reference groups were 1849 healthy control participants (based on amyloid PET) and an independent sample of 1369 AD participants (based on autopsy). MAIN OUTCOMES AND MEASURES: Estimated prevalence of positive amyloid PET scans according to diagnosis, age, and apolipoprotein E (APOE) ε4 status, using the generalized estimating equations method. RESULTS: The likelihood of amyloid positivity was associated with age and APOE ε4 status. In AD dementia, the prevalence of amyloid positivity decreased from age 50 to 90 years in APOE ε4 noncarriers (86% [95% CI, 73%-94%] at 50 years to 68% [95% CI, 57%-77%] at 90 years; n = 377) and to a lesser degree in APOE ε4 carriers (97% [95% CI, 92%-99%] at 50 years to 90% [95% CI, 83%-94%] at 90 years; n = 593; P < .01). Similar associations of age and APOE ε4 with amyloid positivity were observed in participants with AD dementia at autopsy. In most non-AD dementias, amyloid positivity increased with both age (from 60 to 80 years) and APOE ε4 carriership (dementia with Lewy bodies: carriers [n = 16], 63% [95% CI, 48%-80%] at 60 years to 83% [95% CI, 67%-92%] at 80 years; noncarriers [n = 18], 29% [95% CI, 15%-50%] at 60 years to 54% [95% CI, 30%-77%] at 80 years; frontotemporal dementia: carriers [n = 48], 19% [95% CI, 12%-28%] at 60 years to 43% [95% CI, 35%-50%] at 80 years; noncarriers [n = 160], 5% [95% CI, 3%-8%] at 60 years to 14% [95% CI, 11%-18%] at 80 years; vascular dementia: carriers [n = 30], 25% [95% CI, 9%-52%] at 60 years to 64% [95% CI, 49%-77%] at 80 years; noncarriers [n = 77], 7% [95% CI, 3%-18%] at 60 years to 29% [95% CI, 17%-43%] at 80 years. CONCLUSIONS AND RELEVANCE: Among participants with dementia, the prevalence of amyloid positivity was associated with clinical diagnosis, age, and APOE genotype. These findings indicate the potential clinical utility of amyloid imaging for differential diagnosis in early-onset dementia and to support the clinical diagnosis of participants with AD dementia and noncarrier APOE ε4 status who are older than 70 years

Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than once