Unconventional markers of Alzheimer Disease

Although typically conceptualized as a cortical disease, recent neuropathological and neuroimaging investigations on Alzheimer Disease suggest that other brain structures play an important role in the pathogenesis and progression of this devastating condition. In this thesis, we explored novel markers of Alzheimer Disease beyond the classical cortical pathology measures of amyloid, tau, and neurodegeneration. We focused on the role of white matter abnormalities, assessed with magnetic resonance imaging but also with amyloid positron emission tomography, in predicting early pathologic changes and disease progression, as well as on the added value of cognition to amyloid, tau, and neurodegeneration biomarkers. Overall, we found that these unconventional markers provide useful information to detect the earliest pathological changes of the disease, providing a better understanding of the mechanisms that lead to amyloid deposition and cognitive decline

Next-to-next-to-eikonal corrections in the CGC

We extend the study of corrections to the eikonal approximation that was initiated in ref. [1] to higher orders. These corrections associated with the finite width of the target are investigated and the gluon propagator in background field is calculated at next-to-next-to-eikonal accuracy. The result is then applied to the single inclusive gluon production cross section at central rapidities and the single transverse spin asymmetry with a transversely polarized target, in pA collisions, in order to analyze these observables beyond the eikonal limit. The next-to-next-to-eikonal corrections to the unpolarized cross section are non-zero and provide the first corrections to the usual k ⊥-factorized expression. In contrast, the eikonal and next-to-next-to-eikonal contributions to the single transverse spin asymmetry vanish, while the next-to-eikonal ones are non-zero.S

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming yearsThis research was partially funded by DTS17/00138 (Instituto de Salud Carlos III) and ED431F 2017/04 project (GAIN-Xunta de Galicia)S

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models

Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of in-cipientAlzheimer'sDisease(AD)dementiainmildcognitiveimpairment(MCI).Focusedonprovidinganearlierand more accurate diagnosis, sophisticated MRI machine learning algorithms have been developed over therecentyears,mostofthemlearningtheirnon-diseasepatternsfromMCIthatremainedstableover2–3years.Inthis work, we analyzed whether these stable MCI over short-term periods are actually appropriate trainingexamples of non-disease patterns. To this aim, we compared the diagnosis of MCI patients at 2 and 5years offollow-up and investigated its impact on the predictive performance of baseline volumetric MRI measures pri-marily involved in AD, i.e., hippocampal and entorhinal cortex volumes. Predictive power was evaluated interms ofthe areaunder the ROCcurve(AUC), sensitivity,andspecificity inatrialsample of248 MCIpatientsfollowed-up over 5years. We further compared the sensitivity in those MCI that converted before 2years andthose that converted after 2years. Our results indicate that 23% of the stable MCI at 2years progressed in thenextthreeyearsandthatMRIvolumetricmeasuresaregoodpredictorsofconversiontoADdementiaevenatthemid-term, showing a better specificity and AUC as follow-up time increases. The combination of hippocampusand entorhinal cortex yielded an AUC that was significantly higher for the 5-year follow-up (AUC=73% at2yearsvs.AUC=84%at5years),aswellasforspecificity(56%vs.71%).Sensitivityshowedanon-significantslightdecrease(81%vs.78%).Remarkably,theperformanceofthismodelwascomparabletomachinelearningmodels at the same follow-up times. MRI correctly identified most of the patients that converted after 2years(with sensitivity>60%), and these patients showed a similar degree of abnormalities to those that convertedbefore 2years. This implies that most of the MCI patients that remained stable over short periods and subse-quentlyprogressedtoADdementiahadevidentatrophiesatbaseline.Therefore,machinelearningmodelsthatuse these patients to learn non-disease patterns are including an important fraction of patients with evidentpathological changes related to the disease, something that might result in reduced performance and lack ofbiological interpretability.This work was partially supported by the project PI16/01416(ISCIIIco-fundedFEDER) and RYC-2015/17430 (RamónyCajal,Pablo Aguiar). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI)(National Institutes of Health Grant U01AG024904) and DODADNI (Department of Defense award number W81XWH-12-2-0012)S

Spill-in counts in the quantification of 18 F-florbetapir on Aβ-negative subjects: the effect of including white matter in the reference region

Background: We aim to provide a systematic study of the impact of white matter (WM) spill-in on the calculation of standardized uptake value ratios (SUVRs) on Aβnegative subjects, and we study the effect of including WM in the reference region as a compensation. In addition, different partial volume correction (PVC) methods are applied and evaluated. Methods: We evaluated magnetic resonance imaging and 18F-AV-45 positron emission tomography data from 122 cognitively normal (CN) patients recruited at the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cortex SUVRs were obtained by using the cerebellar grey matter (CGM) (SUVRCGM) and the whole cerebellum (SUVRWC) as reference regions. The correlations between the different SUVRs and the WM uptake (WM-SUVRCGM) were studied in patients, and in a well-controlled framework based on Monte Carlo (MC) simulation. Activity maps for the MC simulation were derived from ADNI patients by using a voxel-wise iterative process (BrainViset). Ten WM uptakes covering the spectrum of WM values obtained from patient data were simulated for different patients. Three different PVC methods were tested (a) the regional voxel-based (RBV), (b) the iterative Yang (iY), and (c) a simplified analytical correction derived from our MC simulation. Results: WM-SUVRCGM followed a normal distribution with an average of 1.79 and a standard deviation of 0.243 (13.6%). SUVRCGM was linearly correlated to WM-SUVRCGM (r = 0.82, linear fit slope = 0.28). SUVRWC was linearly correlated to WM-SUVRCGM (r = 0.64, linear fit slope = 0.13). Our MC results showed that these correlations are compatible with those produced by isolated spill-in effect (slopes of 0.23 and 0.11). The impact of the spill-in was mitigated by using PVC for SUVRCGM (slopes of 0.06 and 0.07 for iY and RBV), while SUVRWC showed a negative correlation with SUVRCGM after PVC. The proposed analytical correction also reduced the observed correlations when applied to patient data (r = 0.27 for SUVRCGM, r = 0.18 for SUVRWC). Conclusions: There is a high correlation between WM uptake and the measured SUVR due to spill-in effect, and that this effect is reduced when including WM in the reference region. We also evaluated the performance of PVC, and we proposed an analytical correction that can be applied to preprocessed data.This work was partially supported by the project PI16/01416 (ISCIII co-funded FEDER) and RYC-2015/17430(Ramón y Cajal, PA)S

Diagnostic performance and prediction of clinical progression of plasma phospho-tau181 in the alzheimer's disease neuroimaging initiative

Whilst cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers for amyloid-β (Aβ) and tau pathologies are accurate for the diagnosis of Alzheimer's disease (AD), their broad implementation in clinical and trial settings are restricted by high cost and limited accessibility. Plasma phosphorylated-tau181 (p-tau181) is a promising blood-based biomarker that is specific for AD, correlates with cerebral Aβ and tau pathology, and predicts future cognitive decline. In this study, we report the performance of p-tau181 in >1000 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU), mild cognitive impairment (MCI) and AD dementia patients characterized by Aβ PET. We confirmed that plasma p-tau181 is increased at the preclinical stage of Alzheimer and further increases in MCI and AD dementia. Individuals clinically classified as AD dementia but having negative Aβ PET scans show little increase but plasma p-tau181 is increased if CSF Aβ has already changed prior to Aβ PET changes. Despite being a multicenter study, plasma p-tau181 demonstrated high diagnostic accuracy to identify AD dementia (AUC = 85.3%; 95% CI, 81.4-89.2%), as well as to distinguish between Aβ- and Aβ+ individuals along the Alzheimer's continuum (AUC = 76.9%; 95% CI, 74.0-79.8%). Higher baseline concentrations of plasma p-tau181 accurately predicted future dementia and performed comparably to the baseline prediction of CSF p-tau181. Longitudinal measurements of plasma p-tau181 revealed low intra-individual variability, which could be of potential benefit in disease-modifying trials seeking a measurable response to a therapeutic target. This study adds significant weight to the growing body of evidence in the use of plasma p-tau181 as a non-invasive diagnostic and prognostic tool for AD, regardless of clinical stage, which would be of great benefit in clinical practice and a large cost-saving in clinical trial recruitment.Data collection and sharing was funded by ADNI (NIH #U01 AG024904) and DOD ADNI (#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; Eisa i 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 & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Ph armaceuticals 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 Neuro Imaging at the University of Southern California. TKK holds a Brightfocus fellowship (#A2020812F), and is further supported by the Swedish Alzheimer Foundation (Alzheimerfonden; #AF-930627), the Swedish Brain Foundation (Hjärnfonden; #FO2020-0240), the Swedish Dementia Foundation (Demensförbundet), the Agneta Prytz-Folkes & Gösta Folkes Foundation (#2020-00124), the Aina (Ann) Wallströms and Mary-Ann Sjöbloms Foundation, the Anna Lisa and Brother Björnsson’s Foundation, Gamla Tjänarinnor, and the Gun and Bertil Stohnes Foundation. NJA is supported by the Swedish Alzheimer Foundation (Alzheimerfonden; #AF-931009), the Swedish Brain Foundation (Hjärnfonden), the Agneta Prytz-Folkes & Gösta Folkes Foundation, and the Swedish Dementia Foundation (Demensförbundet). AS was supported by the Emil Aaltonen Foundation and the Paul o Foundation, and currently receives funding from the Orion Research Foundation. MS-C received funding fro m the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skl odowska-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). PR-N is supported by the Weston Brain Institute, the Canadian Institutes of Health Research, the Canadian Consortium on Neurodegeneration in Aging and the Fonds de Recherche du Québec – Santé (FRQS; Chercheur Boursier, and 2020-VICO-279314 TRIAD/BIOVIE Cohort), the CIHR-CCNA Canadian Consortium of Neurodegeneration in Aging, and the Canada Foundation for Innovation (project 34874). KB was supported by the Alzheimer Drug Discovery Foundation (ADDF; #RDAPB- 201809-2016615), the Swedish Research Council (#2017-00915), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017-0243), and a grant (#ALFGBG-715986) from the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement. KB is supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017- 0243), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF- agreement (#ALFGBG-715986), and European Union Joint Program for Neurodegenerative Disorders (JPND2019- 466-236). HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712), Swedish State Support for Clinical Research (# ALFGBG-720931), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), and the UK Dementia Research Institute at UCL