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Purpose SimPET (www.sim-pet.org) is a free cloud-based platform for the generation of realistic brain positron emission tomography (PET) data. In this work, we introduce the key features of the platform. In addition, we validate the platform by performing a comparison between simulated healthy brain FDG-PET images and real healthy subject data for three commercial scanners (GE Advance NXi, GE Discovery ST, and Siemens Biograph mCT). Methods The platform provides a graphical user interface to a set of automatic scripts taking care of the code execution for the phantom generation, simulation (SimSET), and tomographic image reconstruction (STIR). We characterize the performance using activity and attenuation maps derived from PET/CT and MRI data of 25 healthy subjects acquired with a GE Discovery ST. We then use the created maps to generate synthetic data for the GE Discovery ST, the GE Advance NXi, and the Siemens Biograph mCT. The validation was carried out by evaluating Bland-Altman differences between real and simulated images for each scanner. In addition, SPM voxel-wise comparison was performed to highlight regional differences. Examples for amyloid PET and for the generation of ground-truth pathological patients are included. Results The platform can be efficiently used for generating realistic simulated FDG-PET images in a reasonable amount of time. The validation showed small differences between SimPET and acquired FDG-PET images, with errors below 10% for 98.09% (GE Discovery ST), 95.09% (GE Advance NXi), and 91.35% (Siemens Biograph mCT) of the voxels. Nevertheless, our SPM analysis showed significant regional differences between the simulated images and real healthy patients, and thus, the use of the platform for converting control subject databases between different scanners requires further investigation. Conclusions The presented platform can potentially allow scientists in clinical and research settings to perform MC simulation experiments without the need for high-end hardware or advanced computing knowledge and in a reasonable amount of time.

Background: CSF Aß42 is thought to show AD-related alterations earlier than amyloid-ß PET. Therefore, cognitively unimpaired (CU) individuals with abnormal CSF Aß42 and normal amyloid-ß PET are believed to be in the earliest stages of the AD continuum. In this work, we sought to detect structural cerebral alterations in CU individuals with discordant status in these amyloid-ß biomarkers using Machine Learning techniques. Method: We included 498 CU individuals from the ALFA+ and ADNI studies with available MRI, amyloid-ß PET and CSF Aß42 measurements, the latter measured with the exploratory Roche NeuroToolKit assays, a panel of automated robust prototype immunoassays. In addition, we calculated Centiloid (CL) values for the PET measurements. Individuals were categorized as CSF-/PET-, CSF+/PET- and CSF+/PET+ according to established cut-offs (CSF Aß42

Background: Plasma biomarkers have demonstrated excellent agreement with established markers of amyloid-ß (Aß) positivity (PET and CSF) to identify patients with symptomatic AD. However, their predictive capacity in cognitively unimpaired (CU) individuals is lower. In this work, we aimed at assessing whether structural MRI features could improve the capacity of machine learning algorithms applied to plasma biomarkers to identify Aß positive CU individuals. Method: We included 344 CU individuals from the ALFA+ study, with available T1w MRI, Aß PET and plasma measurements for p-tau181, p-tau231, and GFAP. We determined the capacity to predict Aß+ according to PET visual read of plasma biomarkers in combination with clinical data (age, sex, education, MMSE, and APOE genotype) and MRI-derived measurements. We trained Random Forest classifiers with clinical, clinical and plasma, clinical and MRI; and clinical, plasma and MRI data. The MRI-selected measurements consisted of Jack’s AD-signature and the two features best predictive of Aß+: left-lat-inf-ventricle and left caudal-anterior-cingulate as determined with Freesurfer 6.0. We conducted ROC and Precision-Recall analyses and calculated savings as the percentage difference of costs between standard trial recruitment and a triaging step with the different classifiers. The threshold for positivity prediction was chosen to maximize savings and the precision-recall ratio. We computed ROC-AUC, PPV, sensitivity and savings as metrics for the comparison. Results: The mean centiloid values of Aß+ subjects (13.66%) and Aß- were 33.73 and -1.82, respectively (Table 1). ROC-AUCs for the classifiers to detect Aß+ was 0.723 for clinical information; 0.861 for clinical and plasma measurements; 0.711 for clinical and MRI; and 0.871 for clinical, plasma and MRI. Savings associated with the best classifier (clinical + plasma + MRI) would translate in savings (95%CI) of 52.8% (49.0, 56.3) in recruitment costs. See Figure 1 for all the metrics’ results. Conclusion: Machine learning algorithms on plasma biomarkers achieved a high accuracy on predicting a positive visual read on amyloid PET scans in cognitively unimpaired individuals. MRI biomarkers marginally improved the predictive capacity. Used as a triaging method, such an algorithm would result in a reduction of over 50% in the cost to identify participants for secondary prevention trials.

Introduction: Amyloid beta (Aβ) pathology is an Alzheimer's disease early hallmark. Here we assess the value of longitudinal self- and informant reports of cognitive decline to predict Aβ positron emission tomography (PET) outcome in cognitively unimpaired middle-aged individuals. Methods: A total of 261 participants from the ALFA+ study underwent [18F]flutemetamol PET and Subjective Cognitive Decline Questionnaire (SCD-Q) concurrently, and 3 years before scan. We used logistic regressions to evaluate the ability of SCD-Q scores (self and informant) to predict Aβ PET visual read, and repeated analysis of variance to assess whether changes in SCD-Q scores relate to Aβ status. Results: Self-perception of decline in memory (odds ratio [OR] = 1.2), and informant perception of executive decline (OR = 1.6), increased the probability of a positive scan. Informant reports 3 years before scanning predicted Aβ PET outcome. Longitudinal increase of self-reported executive decline was predictive of Aβ in women (P = .003). Discussion: Subjective reports of cognitive decline are useful to predict Aβ and may improve recruitment strategies. The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004 and the Alzheimer's Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa‐17‐519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2017‐SGR‐892. Marc Suárez‐Calvet received funding from the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska‐Curie action grant agreement No 752310, and currently receives funding from Instituto de Salud Carlos III (PI19/00155) and from the Spanish Ministry of Science, Innovation, and Universities (Juan de la Cierva Programme grant IJC2018‐037478‐I). Juan Domingo Gispert is supported by the Spanish Ministry of Science and Innovation (RYC‐2013‐13054). Oriol Grau‐Rivera is supported by the Spanish Ministry of Science, Innovation, and Universities (FJCI‐2017‐33437). ASV is the recipient of an Instituto de Salud Carlos III Miguel Servet II fellowship (CP II 17/00029). Eider M. Arenaza‐Urquijo is supported by the Spanish Ministry of Science, Innovation and Universities–Spanish State Research Agency (RYC2018‐026053‐I). Carolina Minguillon was supported by the Spanish Ministry of Economy and Competitiveness (grant n° IEDI‐2016‐00690).

Objective: To determine the usefulness of dopamine transporter (DAT) imaging to identify idiopathic REM sleep behavior disorder (IRBD) patients at risk for short-term development of clinically-defined synucleinopathy. Methods: Eighty-seven patients with polysomnography-confirmed IRBD underwent 123I-FP-CIT DAT-SPECT. Results were compared with 20 matched controls without RBD who underwent DAT-SPECT. In patients, FP-CIT uptake was considered abnormal when values were two standard deviations below controls' mean uptake. After DAT-SPECT, patients were followed-up during 5.7 ± 2.2 (range, 2.6-9.9) years. Results: Baseline DAT deficit was found in 51 (58.6%) patients. During follow-up 25 (28.7%) subjects developed clinically-defined synucleinopathy (Parkinson disease in 11, dementia with Lewy bodies in 13, multiple system atrophy in one) with mean latency of 3.2 ± 1.9 years from imaging. Kaplan-Meier survival analysis showed increased risk of incident synucleinopathy in patients with abnormal DAT-SPECT than with normal DAT-SPECT (20% vs. 6% at three years, 33% vs. 18% at five years; log rank test, p=0.006). Receiver operating characteristics curve revealed that reduction of FP-CIT uptake in putamen greater than 25% discriminated patients with DAT deficit who developed synucleinopathy from patients with DAT deficit that remained disease-free after three years of follow-up. At 5-year follow-up, DAT-SPECT had 75% sensitivity, 51% specificity, 44% positive predictive value, 80% negative predictive value, and like-hood ratio 1.54 to predict synucleinopathy. Interpretation: DAT-SPECT identifies IRBD patients at short-term risk for synucleinopathy. Decreased FP-CIT putamen uptake greater than 25% predicts synucleinopathy after three years follow-up. These observations may be useful to select candidates for disease-modification trials in IRBD. This article is protected by copyright. All rights reserved.

The 6-hydroxydopamine (6-OHDA) rodent model of Parkinson's disease (PD) has been used to evaluate the nigrostriatal pathway. The aim of this work was to explore the relationship between the degree of 6-OHDA-induced dopaminergic degeneration and [(123)I]FP-CIT binding using single photon emission computed tomography (SPECT). Fourteen rats received a 6-OHDA injection (4 or 8 µg) into the left medial forebrain bundle. After 3 weeks, magnetic resonance imaging and scans with a small-animal SPECT system were performed. Finally, the nigrostriatal lesion was assessed by immunohistochemical analysis. Immunohistochemical analysis confirmed two levels of dopaminergic degeneration. Lesions induced by 6-OHDA diminished the ipsilateral [(123)I]FP-CIT binding by 61 and 76%, respectively. The decrease in tracer uptake between control and lesioned animals was statistically significant, as was the difference between the two 6-OHDA lesioned groups. Results concluded that [(123)I]FP-CIT SPECT is a useful technique to discriminate the degree of dopaminergic degeneration in a rat model of PD.