Obrazowanie tensora dyfuzji u pacjentów z chorobą Alzheimera i łagodnymi zaburzeniami poznawczymi

A wide range of imaging studies provides growing support for the potential role of diffusion tensor imaging (DTI) in evaluating microstructural white matter integrity in Alzheimer disease (AD) and mild cognitive impairment (MCI). Our review aims to present DTI principles, post-processing and analysis frameworks and to report the results of particular studies. The distribution of AD-related white matter abnormalities is widely discussed in the light of deteriorated connectivity within certain tracts due to secondary white matter degeneration; primary alterations are also assumed to contribute to the pattern. The question whether it is more effective to assess the whole-brain diffusion or to directly concentrate on specific regions remains an interesting issue. Assessing white matter microstructure alterations, as evaluated by group-level differences of tensor-derived parameters, may be a promising neuroimaging tool for differential diagnosis between AD, MCI and other cognitive disorders, as well as being particularly helpful in the interpretation of underlying pathological processes.Rosnąca liczba badań naukowych wskazuje na znaczenie obrazowania tensora dyfuzji (DTI) w ocenie mikrostrukturalnej integralności istoty białej w chorobie Alzheimera (ChA) i łagodnych zaburzeniach poznawczych (ŁZP). W niniejszej pracy przeglądowej omówiono zasady obróbki danych oraz analizy DTI i przedstawiono wyniki poszczególnych badań prezentujących różne modele charakterystycznych dla ChA zmian w istocie białej. Szeroko dyskutowane jest rozmieszczenie uszkodzeń w istocie białej, głównie w odniesieniu do wtórnego zwyrodnienia poszczególnych włókien wskutek zaniku istoty szarej lub pierwotnego zwyrodnienia istoty białej. Interesujący i nierozstrzygnięty pozostaje dylemat, czy bardziej efektywne jest obrazowanie zmian dyfuzji w całym mózgu, czy skupianie się na konkretnych strukturach. Zastosowanie DTI w ocenie mikrostrukturalnych zmian zachodzących w istocie białej mózgu może być obiecującym narzędziem w różnicowaniu pomiędzy ChA, ŁZP i innymi zaburzeniami poznawczymi; jest szczególnie przydatne przy interpretacji leżących u ich podłoża procesów patologicznych

Comparative study of CuO supported on CeO2, Ce0.8Zr0.2O2 and Ce0.8Al0.2O2 based catalysts in the CO-PROX reaction

CuO supported on CeO2, Ce0.8Zr0.2O2 and Ce0.8Al0.2O2 based catalysts (6%wt Cu) were synthesized and tested in the preferential oxidation of CO in a H2-rich stream (CO-PROX). Nanocrystalline supports, CeO2 and solid solutions of modified CeO2 with zirconium and aluminum were prepared by a freeze-drying method. CuO was supported by incipient wetness impregnation and calcination at 400 C. All catalysts exhibit high activity in the CO-PROX reaction and selectivity to CO2 at low reaction temperature, being the catalyst supported on CeO2 the more active and stable. The influence of the presence of CO2 and H2O was also studied

Different Profiles of Spatial Navigation Deficits In Alzheimer's Disease Biomarker-Positive Versus Biomarker-Negative Older Adults With Amnestic Mild Cognitive Impairment.

Background: Spatial navigation impairment is a promising cognitive marker of Alzheimer's disease (AD) that can reflect the underlying pathology. Objectives: We assessed spatial navigation performance in AD biomarker positive older adults with amnestic mild cognitive impairment (AD aMCI) vs. those AD biomarker negative (non-AD aMCI), and examined associations between navigation performance, MRI measures of brain atrophy, and cerebrospinal fluid (CSF) biomarkers. Methods: A total of 122 participants with AD aMCI (n = 33), non-AD aMCI (n = 31), mild AD dementia (n = 28), and 30 cognitively normal older adults (CN) underwent cognitive assessment, brain MRI (n = 100 had high-quality images for volumetric analysis) and three virtual navigation tasks focused on route learning (body-centered navigation), wayfinding (world-centered navigation) and perspective taking/wayfinding. Cognitively impaired participants underwent CSF biomarker assessment [amyloid-β1-42, total tau, and phosphorylated tau181 (p-tau181)] and amyloid PET imaging (n = 47 and n = 45, respectively), with a subset having both (n = 19). Results: In route learning, AD aMCI performed worse than non-AD aMCI (p < 0.001), who performed similarly to CN. In wayfinding, aMCI participants performed worse than CN (both p ≤ 0.009) and AD aMCI performed worse than non-AD aMCI in the second task session (p = 0.032). In perspective taking/wayfinding, aMCI participants performed worse than CN (both p ≤ 0.001). AD aMCI and non-AD aMCI did not differ in conventional cognitive tests. Route learning was associated with parietal thickness and amyloid-β1-42, wayfinding was associated with posterior medial temporal lobe (MTL) volume and p-tau181 and perspective taking/wayfinding was correlated with MRI measures of several brain regions and all CSF biomarkers. Conclusion: AD biomarker positive and negative older adults with aMCI had different profiles of spatial navigation deficits that were associated with posterior MTL and parietal atrophy and reflected AD pathology

The reliability of a deep learning model in external memory clinic MRI data: A multi‐cohort study

AbstractBackgroundDeep learning (DL) has provided impressive results in numerous domains in recent years, including medical image analysis. Training DL models requires large data sets to yield good performance. Since medical data can be difficult to acquire, most studies rely on public research cohorts, which often have harmonized scanning protocols and strict exclusion criteria. This is not representative of a clinical setting. In this study, we investigated the performance of a DL model in out‐of‐distribution data from multiple memory clinics and research cohorts.MethodWe trained multiple versions of AVRA: a DL model trained to predict visual ratings of Scheltens' medial temporal atrophy (MTA) scale (Mårtensson et al., 2019). This was done on different combinations of training data—starting with only harmonized MRI data from public research cohorts, and further increasing image heterogeneity in the training set by including external memory clinic data. We assessed the performance in multiple test sets by comparing AVRA's MTA ratings to an experienced radiologist's (who rated all images in this study). Data came from Alzheimer's Disease Neuroimaging Initiative (ADNI), AddNeuroMed, and images from 13 European memory clinics in the E‐DLB consortium.ResultsModels trained only on research cohorts generalized well to new data acquired with similar protocols as the training data (weighted kappa κw between 0.70‐0.72), but worse to memory clinic data with more image variability (κw between 0.34‐0.66). This was most prominent in one specific memory clinic, where the DL model systematically predicted too low MTA scores. When including data from a wider range of scanners and protocols during training, the agreement to the radiologist's ratings in external memory clinics increased (κw between 0.51‐0.71).ConclusionIn this study we showed that increasing heterogeneity in training data improves generalization to out‐of‐distribution data. Our findings suggest that studies assessing reliability of a DL model should be done in multiple cohorts, and that softwares based on DL need to be rigorously evaluated prior to being certified for deployment to clinics. References: Mårtensson, G. et al. (2019) 'AVRA: Automatic Visual Ratings of Atrophy from MRI images using Recurrent Convolutional Neural Networks', NeuroImage: Clinical. Elsevier, 23(March), p. 101872

Validation of the LUMIPULSE automated immunoassay for the measurement of core AD biomarkers in cerebrospinal fluid

Altres ajuts: Swedish Research Council (#2017-00915); Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615); Swedish Alzheimer Foundation (#AF-742881); Hjärnfonden, Sweden (#FO2017-0243); Swedish State Under the Agreement Between the Swedish Government and the County Councils, the ALF-Agreement (#ALFGBG-715986); National Program of Sustainability II (MEYS CR); Ministry of Health of the Czech Republic (grant no. 19-04-00560); ZonMW (part of the Dutch national 'Deltaplan for Dementia'; Selfridges Group Foundation; National Institutes of Health, USA (grant number 5R01NS104147-02); Alzheimerfonden (AF-930934); Åhléns-stiftelsen; Stiftelsen för Gamla tjänarinnor; Canadian Institutes of Health Research (CIHR) (MOP-11-51-31; RFN 152985, 159815, 162303); Canadian Consortium of Neurodegeneration and Aging (CCNA; MOP-11-51-31 -team 1); Weston Brain Institute, the Alzheimer's Association (NIRG-12-92090, NIRP-12-259245); Brain Canada Foundation (CFI Project 34874; 33397); Fonds de Recherche du Québec - Santé (FRQS; Chercheur Boursier, 2020-VICO-279314); Wallenberg Scholar supported by grants, Swedish Research Council (#2018-02532); Swedish State Support for Clinical Research (#ALFGBG-720931); Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862); Dementia Research Institute at UCL.Objectives: The core cerebrospinal fluid (CSF) biomarkers; total tau (tTau), phospho-tau (pTau), amyloid β 1-42 (Aβ 1-42), and the Aβ 1-42/Aβ 1-40 ratio have transformed Alzheimer's disease (AD) research and are today increasingly used in clinical routine laboratories as diagnostic tools. Fully automated immunoassay instruments with ready-to-use assay kits and calibrators has simplified their analysis and improved reproducibility of measurements. We evaluated the analytical performance of the fully automated immunoassay instrument LUMIPULSE G (Fujirebio) for measurement of the four core AD CSF biomarkers and determined cutpoints for AD diagnosis. Methods: Comparison of the LUMIPULSE G assays was performed with the established INNOTEST ELISAs (Fujirebio) for hTau Ag, pTau 181, β-amyloid 1-42, and with V-PLEX Plus Aβ Peptide Panel 1 (6E10) (Meso Scale Discovery) for Aβ 1-42/Aβ 1-40, as well as with a LC-MS reference method for Aβ 1-42. Intra- and inter-laboratory reproducibility was evaluated for all assays. Clinical cutpoints for Aβ 1-42, tTau, and pTau was determined by analysis of three cohorts of clinically diagnosed patients, comprising 651 CSF samples. For the Aβ 1-42/Aβ 1-40 ratio, the cutpoint was determined by mixture model analysis of 2,782 CSF samples. Results: The LUMIPULSE G assays showed strong correlation to all other immunoassays (r>0.93 for all assays). The repeatability (intra-laboratory) CVs ranged between 2.0 and 5.6%, with the highest variation observed for β-amyloid 1-40. The reproducibility (inter-laboratory) CVs ranged between 2.1 and 6.5%, with the highest variation observed for β-amyloid 1-42. The clinical cutpoints for AD were determined to be 409 ng/L for total tau, 50.2 ng/L for pTau 181, 526 ng/L for β-amyloid 1-42, and 0.072 for the Aβ 1-42/Aβ 1-40 ratio. Conclusions: Our results suggest that the LUMIPULSE G assays for the CSF AD biomarkers are fit for purpose in clinical laboratory practice. Further, they corroborate earlier presented reference limits for the biomarkers

Cognitive and Motor Decline in Dementia with Lewy Bodies and Parkinson's Disease Dementia

Funding Information: The University of Stavanger supported M.C.G. The CamPaIGN study has received funding from the Wellcome Trust, the Medical Research Council, the Patrick Berthoud Trust, and the NIHR Cambridge Biomedical Research Centre (BRC‐1215‐20014). The ICICLE‐PD study was funded by Parkinson's UK (J‐0802, G‐1301, G‐1507) and supported by the Lockhart Parkinson's Disease Research Fund, National Institute for Health Research (NIHR) Newcastle Biomedical Research Unit and Centre based at Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. The PICNICS study was funded by the Cure Parkinson's Trust, the Van Geest Foundation, the Medical Research Council, Parkinson's UK, and the NIHR Cambridge Biomedical Research Centre (BRC‐1215‐20014). The NYPUM study was supported by grants from the Swedish Medical Research Council, Erling‐Persson Foundation, the Swedish Brain Foundation (Hjärnfonden), Umeå University, Västerbotten County Council, King Gustaf V and Queen Victoria Freemason Foundation, Swedish Parkinson Foundation, Swedish Parkinson Research Foundation, Kempe Foundation, Swedish PD Association, the European Research Council, and the Knut and Alice Wallenberg Foundation. The PINE study was funded by Parkinson's UK (grant numbers G0502, G0914, and G1302), the Scottish Chief Scientist Office (CAF/12/05, PCL/17/10), Academy of Medical Sciences, NHS Grampian endowments, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, and SPRING. The PARKWEST study was supported by the Research Council of Norway (grant# 177966), the Western Norway Regional Health Authority (grant# 911218 and # 911949), Reberg legacy and the Norwegian Parkinson's Research Foundation. The PICC collaboration has been supported by The Chief Scientist Office of the Scottish Government (PCL/17/10), the Academy of Medical Sciences, Parkinson's UK (initial collaborator meeting) and the Norwegian Association for Public Health. The DEMVEST Study was supported by the regional health authorities of Western Norway, Helse‐Vest (grant# 911973). Motol University Hospital's Czech Brain Aging Study was supported by the National Institute for Neurological Research (Programme EXCELES, ID Project No. LX22NPO5107)—Funded by the European Union—Next Generation EU and by Charles University grant PRIMUS 22/MED/011. The Sant Pau Initiative on Neurodegeration (SPIN) cohort was supported by the Fondo de Investigaciones Sanitario (FIS), Instituto de Salud Carlos III (PI14/01126, PI17/01019 and PI20/01473 to JF, PI13/01532 and PI16/01825 to RB, PI18/00335 to MCI, PI18/00435 and INT19/00016 to DA, PI17/01896 and AC19/00103to AL) and the CIBERNED program (Program 1, Alzheimer Disease to AL), jointly funded by Fondo Europeo de Desarrollo Regional, Unión Europea, “Una manera de hacer Europa”. It was also supported by the National Institutes of Health (NIA grants 1R01AG056850‐01A1; R21AG056974; and R01AG061566), by Generalitat de Catalunya (2017‐SGR‐547, SLT006/17/125, SLT006/17/119, SLT002/16/408) and “Marató TV3” foundation grants 20141210, 044412 and 20142610. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The sponsors were not involved in the study design, in the collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the article for publication. The authors declare that there are no conflicts of interest relevant to this work. Funding Sources and Conflicts of Interest:Peer reviewedPublisher PD

FTLD-TDP with motor neuron disease, visuospatial impairment and a progressive supranuclear palsy-like syndrome: broadening the clinical phenotype of TDP-43 proteinopathies. A report of three cases

<p>Abstract</p> <p>Background</p> <p>Frontotemporal lobar degeneration with ubiquitin and TDP-43 positive neuronal inclusions represents a novel entity (FTLD-TDP) that may be associated with motor neuron disease (FTLD-MND); involvement of extrapyramidal and other systems has also been reported.</p> <p>Case presentation</p> <p>We present three cases with similar clinical symptoms, including Parkinsonism, supranuclear gaze palsy, visuospatial impairment and a behavioral variant of frontotemporal dementia, associated with either clinically possible or definite MND. Neuropathological examination revealed hallmarks of FTLD-TDP with major involvement of subcortical and, in particular, mesencephalic structures. These cases differed in onset and progression of clinical manifestations as well as distribution of histopathological changes in the brain and spinal cord. Two cases were sporadic, whereas the third case had a pathological variation in the progranulin gene 102 delC.</p> <p>Conclusions</p> <p>Association of a "progressive supranuclear palsy-like" syndrome with marked visuospatial impairment, motor neuron disease and early behavioral disturbances may represent a clinically distinct phenotype of FTLD-TDP. Our observations further support the concept that TDP-43 proteinopathies represent a spectrum of disorders, where preferential localization of pathogenetic inclusions and neuronal cell loss defines clinical phenotypes ranging from frontotemporal dementia with or without motor neuron disease, to corticobasal syndrome and to a progressive supranuclear palsy-like syndrome.</p