Search

Background: In preclinical Alzheimer’s disease (AD), amyloid accumulates in highly-functionally connected brain regions. This selective vulnerability is related to the high neuronal fluctuations, typical of these regions. Dynamic functional connectivity (FC) was introduced to investigate network organization over time, with high network variations indicating regional flexibility, hence promoting functional integration. The relation of early amyloid deposition with FC dynamics remains unclear. Eigenvector centrality (EC) evaluates a node's importance in functional networks, both for the whole functional MRI time series (“static” EC) or within sliding-windows (“dynamic” EC). We studied the association of cerebrospinal fluid (CSF) amyloid load with static and dynamic EC in non-demented individuals from the European Prevention of Alzheimer’s Dementia (EPAD) cohort. Methods: Data for 701 non-demented participants were available. CSF Aβ1-42 levels

Background: Brain amyloid-β (Aβ) is the pathological hallmark of Alzheimer's disease (AD). In logistic disease models, Aβ accumulation is a sigmoid function of time-since-disease-onset (TSDO) (figure 1). Previous positron emission tomography (PET)-based models vary accumulation onset(t50) and duration(r) globally; capacity(K) and baseline(NS) regionally (Whittington2018). We confirm existing approaches and propose a more powerful ICA-based approach to quantify disease severity and estimate TSDO. Method: We used 1071 18F-florbetapir standard uptake value ratio (SUVR) images from the ADNI-2 study (adni.loni.usc.edu/data-samples/data-types/pet). Images were mapped into MNI space. Averages were extracted using the Harvard-Oxford brain-atlas. Whole-brain tracer-specific sigmoid parameters (Jack2013) obtained from the literature were used to estimate TSDO. Of 16 models of regional Aβ accumulation (each of the 4 regional sigmoid parameters varied either regionally or globally), the optimal Bayesian information criterion was found with global t50 and r, and regional NS and K (figure 1) with global values r=6.16y and t50=4.10y. Linearised maps of NS and K were obtained by regressing the SUVR maps onto the global sigmoid. We also estimated these maps as independent components, using a 2-component ICA on the SUVR maps. Both outcomes were used to quantify Aβ accumulation from SUVR images as weighting factors of the accumulation map. We compared the weights from the logistic model and the ICA model in ADNI, using effect size measured with Hedges' g between cognitively normal (CN), subjective memory complaints (SMC), mild cognitive impairment (EMCI/MCI/LMCI) and AD groups. We compared 3 longitudinal visits (N=112) in the OASIS-3 study (see www.oasis-brains.org) with both methods, global SUVR and Centiloid (Klunk2015) using 11C-PiB PET SUVR images. Result: Maps of accumulation capacity from both models had spatial correlation of 0.86 (figure 2); baseline maps had spatial correlation of 0.95. Hedges' g between ADNI groups was 2.25 for K, and 2.42 for ICA (1.46 for global SUVR). In OASIS-3, Hedges' g between visits was 1.24 for K, 1.46 for ICA (global SUVR 0.15, Centiloid 0.4). Conclusion: We demonstrate that linear accumulation models can be used to quantify brain Aβ with PET; maps obtained by ICA yield larger effect sizes than the logistic method for differentiating groups and measuring changes between visits.

Introduction: we investigated amyloid-burden quantification in a mixed memory clinic population. Methods: [18 F]Florbetaben amyloid-PET (positron emission tomography) scans of 348 patients were visually read and quantified using the Centiloid (CL) method. General linear models were used to assess CL differences across syndromic and etiological diagnosis. Linear mixed models were fitted to assess the predictive value of visual read (VR) and CL on longitudinal Mini-Mental Status Examination (MMSE). Results: CL was associated with syndromic (F = 4.42, p = 0.014) and etiological diagnosis (F = -12.66, p < 0.001), with Alzheimer's disease (AD) patients showing the highest amyloid burden (62.9 ± 27.5), followed by dementia with Lewy bodies (DLB) (25.3 ± 35.5) and cardiovascular disease (CVD) (16.7 ± 24.5), and finally frontotemporal lobe degeneration (FTLD) (5.0 ± 17.22, t = -12.66, p < 0.001). CL remained predictive of etiological diagnosis (t = -2.41, p = 0.017) within the VR+ population (N = 157). VR was not a significant predictor of MMSE (t = -1.53, p = 0.13) for the SCD population (N = 90), whereas CL was (t = -3.30, p = 0.001). Discussion: the extent of amyloid pathology through quantification holds clinical value, potentially in the context of differential diagnosis as well as prognosis. Research of the Alzheimer Centre Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. The Alzheimer Centre Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. WMvdF holds the Pasman chair. WMvdF is recipient of the collaboration project ABIDE-clinical utility, which is co-funded by the PPP Allowance made available by health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships and Life Molecular Imaging GmbH (grant no. LSHM18075). WMvdF is recipient of ABOARD, which is a public-private partnership receiving funding from ZonMW (#73305095007), Alzheimer Nederland and Health∼Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). More than 30 partners contribute to ABOARD. ABIDE has been funded in the context of the Dutch national dementia plan (project number: 733050201). The project leading to this article has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives the support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This communication reflects the views of the authors and neither the IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.

Background:Arterial spin labelling (ASL), greymatter (GM) densities and structural volumes have been shown to be strong predictors of dementia in machine learning classification studies [1,2]. Many of these experiments, however, were performed on single modalities in small 'matched' cohorts using voxels, which introduces noise, increases computation time, and limits interpretability and generalisability. We propose an automated classification pipeline thatworks in the patients' native T1 and ASL MRI spaces, is based on anatomically meaningful regions of interest (ROIs), and iteratively selects predictive biomarkers. Methods: 280 patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI) or probableAlzheimer's disease (AD) were included in the study. For all subjects T1 and ASL MRI as well as basic demographics such as age and sex were available (see Table 1). All subjects' MRI scans underwent brain tissue segmentation and parcellation using geodesic information flows (GIF) [3] yielding 143 ROIs per subject. Each ROI was associated with mean GMdensity, structural volume and meanASL perfusion.After α-priori exclusion of non-informative regions such as background or skull, 396 features were used for experiments. Support vector machine (SVM) models were used to differentiate pairwise between AD, MCI, and SCD. 50% of all subjects from each class were randomly assigned to either a training or a testing set to create and validate the classifier, and permuted 1000 times. The classifier assigns weights to each feature and our model recursively removes the 20% of features with the lowest average weight over 1000 repetitions. Results: In all three experiments, the highest accuracies can be observed when only 42- 66 out of 396 features are used (see Figure 1). The maximum obtained accuracies are 91.6% for AD vs SCD, 85.3% for AD vs MCI, and 80.1% for MCI vs SCD. The majority of selected features are structural volumes (51%-60%) but ASL also contributes strongly to the classification outcome with ≤33% of biomarkers. An illustration of selected ROIs is shown in Figure 2. Conclusions:Our SVM model provides a stable mechanism to automatically identify multimodal MRI biomarkers relevant to the diagnosis of dementia whilst surpassing the classification accuracy of previous unimodal studies [1,2]. (Figure Presented).

Introduction: The value of quantitative longitudinal and regional amyloid beta (Aβ) measurements in predicting cognitive decline in initially cognitively unimpaired (CU) individuals remains to be determined. Methods: We selected 133 CU individuals with two or more [11C]Pittsburgh compound B ([11C]PiB) scans and neuropsychological data from Open Access Series of Imaging Studies (OASIS-3). Baseline and annualized distribution volume ratios were computed for a global composite and four regional clusters. The predictive value of Aβ measurements (baseline, slope, and interaction) on longitudinal cognitive performance was examined. Results: Global performance could only be predicted by Aβ burden in an early cluster (precuneus, lateral orbitofrontal, and insula) and the precuneus region of interest (ROI) by itself significantly improved the model. Precuneal Aβ burden was also predictive of immediate and delayed episodic memory performance. In Aβ subjects at baseline (N = 93), lateral orbitofrontal Aβ burden predicted working and semantic memory performance. Discussion: Quantifying longitudinal and regional changes in Aβ can improve the prediction of cognitive functioning in initially CU individuals. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 115952. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA. This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. FB is supported by the NIHR UCLH biomedical research center. FB and AMW are supported by the European Union's Horizon 2020 research and innovation program under grant agreement No. 666992. Data were provided by OASIS‐3. Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P50AG00561, P30NS09857781, P01AG026276, P01AG003991, R01AG043434, UL1TR000448, and R01EB009352.