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RIN-Abstracts-Book.Pdf ABSTRACTS BOOK ANNUAL MEETING 8-9 luglio 2020 Prima edizione reteneuroscienze.it DEMENZE E-INFRASTRUCTURES AVAILABLE TO RIN FOR BIG-DATA ANALYSIS Silvia De Francesco1,2, Fulvia Palesi3, Samantha Galluzzi2, Cristina Muscio4, Pietro Tiraboschi4, Anna Nigri7, Gabriella Bottini 3,9, Maria Grazia Bruzzone7, Fabrizio Tagliavini4, Giovanni B. Frisoni2,10, Philippe Ryvlin11, Jean-François Demonet11, Ferath Kherif11, Stefano F. Cappa8,12, Claudia AM Gandini Wheeler- Kingshott3,5,8, Egidio D’Angelo3,8, Alberto Redolfi1, for the ADNI – HBP – Italian Network of Neuroscience and Neurorehabilitation (RIN) neuroimaging initiatives 2 1 Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, 3 IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy;4 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy;5 Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy;6 NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK;7 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy;8 Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy;9 IRCCS Mondino Foundation, Pavia, Italy;10 Cognitive Neuropsychology Center, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy;11 Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland;12 Department of Clinical Neurosciences, Leenaards Memory Centre,13 Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland;14 University School of Advanced Studies, Pavia, Italy. Background: The Italian IRCCS neuroimaging network of Neuroscience and Rehabilitation (RIN) promotes the exchange of data and results encouraging innovative research. Two publicly available platforms are currently used for big-data analyses by the RIN: (i) neuGRID (https://www.neugrid2.eu) which allows efficient storage of data as well as single-case analysis; and (ii) the Medical Informatics Platform (MIP), developed by the HBP (https://www.humanbrainproject.eu/), which provides machine learning (ML) tools to perform holistic analyses. This study explored the complementarity and feasibility of both platforms through a fine-grained stratification of patients in the AD spectrum, integrating clinical, CSF, genetics, imaging quantifications with semi-unsupervised ML tools. Metodi: neuGRID is a HPC platform with 150TB of storage and 1.8TB of RAM memory. NeuGRID hosts, among the others (Fig.1), the RIN dataset covering 300 elderly people with different types of dementia (AD, FTD, LBD) and 150 controls. NeuGRID stores also the phantom data (511 scans) of the RIN harmonization phase. The algorithms in neuGRID allow to measure surrogate imaging biomarkers as a proxy of neurodegeneration, such as: hippocampal volume, cortical thickness, WM hyperintensity, brain hypometabolism. MIP, in contrast, is a grid-cloud e-infrastructure distributed in many European as well as in 5 Italian hospitals (Carlo Besta - Milan; FBF - Brescia; CHT Niguarda - Pavia/Milan; Don Gnocchi - Milan; San Camillo - Venice). It offers advanced ML algorithms (Gradient-Boosting [Friedman, 2002]; CCC [Mitelpunkt, 2015]) for an advanced clusterization of the patients. Data hosted in neuGRID and MIP are used to train/test the algorithms of the platforms. To reach our aim we trained/tested the CCC algorithm on 432 CN, 456 MCI, 451 AD subjects selected from EDSD, ADNI, and the Italian MIPs. We used a nested 10-fold cross validation strategy to find new potential homogeneous clusters. Risultati: Fig.2 shows the semi-unsupervised clusterization results of CCC algorithm. Each cluster was cross-validated in a synoptic table against original diagnostic classes. Conclusioni: RIN can leverage on cross-linked platforms combining big-data and advanced ML algorithms. These platforms represent innovative second opinion tools. The combination of semi- unsupervised tools with informative biomarkers generates novel patient strata which may aid to better understand the neurodegenerative disease evolution. INFLUENCE OF AGE AND COGNITIVE RESERVE ON BRAIN METABOLISM IN PATIENTS WITH DEMENTIA WITH LEWY BODIES (DLB): AN EUROPEAN DLB CONSORTIUM STUDY Matteo Bauckneht1,2, Andrea Chincarini3, Enrico Peira3, Stefano Raffa1,2, Dario Arnaldi1,4, Matteo Pardini1,4, Maria Isabella Donegani1,2, Alberto Miceli1,2, Michele Balma1,2, Nicola Girtler1,4, Gianmario Sambuceti1,2, Dag Aarsland5,6, Flavio Nobili1,4, Silvia Morbelli1,2 for the E-DLB consortium. 1. IRCCS Policlinico San Martino Genoa; 2. Nuclear Medicine Unit, Department of Health Sciences, University of Genoa; 3. National Institute of Nuclear Physics (INFN), Genoa section, Genoa, Italy; 4. Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa, Italy; 5. Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway; 6. Wolfson Centre for Age-Related Diseases, King's College London, London, UK. Background: In a previous project of the E-DLB consortium we identified brain regions whose metabolic impairment contributes to the DLB-related FDG-PET pattern across the four core features. In the same study we highlighted a significant influence of the severity of global cognitive impairment on the expression of this pattern. In this study we aimed to assess the influence of age and cognitive reserve (CR) on the DLB- related FDG-PET pattern. Methods: Brain FDG-PET and clinical/demographic information were available in 171 patients recruited in 11 centers belonging to the E-DLB consortium (age 73.4±7.0; 93 males; MMSE score 22.2±4.7; Education 10.4±4.0 years). Principal component analysis was applied to identify brain regions relevant to the local data variance. A linear regression model was applied to control core feature-specific patterns for the effect of age and education (as a surrogate of CR). Finally, a regression analysis to the locally normalized intensities was performed to generate an age-sensitive map as well as a CR-sensitive map (both maps were also adjusted for: MMSE score, Gender, and Center). Results: Education negatively covaried with metabolism in insula, medial frontal gyrus, hippocampus in the both hemispheres and positively covaried with metabolism in left parietal cortex, and bilateral precuneus (p<0.001). Influence of CR was thus able to make more evident the DLB-typical cingulate-island sign. Similarly, a positive covariance with education was highlighted for the expression of the metabolic patterns associated with clinical core features thus showing that more educated patients express more heavily the metabolic patterns with their components of relative hypo- and hyper-metabolism. Finally, age negatively covaried with metabolism in bilateral posterior cingulate thus reducing the expression of the cingulate-island sign (p<0.001). An effect that we previously demonstrated also for MMSE score. Conclusions: Cognitive reserve-related mechanisms are at work in DLB patients and affect both the general DLB-r elated pattern as well as the expression of core-features typical patterns. The DLB-typical cingulate- island sign may be blunted in older DLB patients, as well as in patients with severe dementia. This finding suggests the need of looking for other DLB metabolic typical features in older patients and in patients with higher severity of global impairment. DIFFERENT OCCUPATIONAL PROFILES MODULATE METABOLIC CONNECTIVITY IN DEMENTIA WITH LEWY BODIES Cecilia Boccalini 1, Giulia Carli1,2, Giovanna Vanoli2, Massimo Filippi 3,4, Sandro Iannaccone 5, Giuseppe Magnani 6, Daniela Perani 1,2 1 School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; 2 Division of Neuroscience, In vivo structural and molecular neuroimaging unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; 3School of Medicine, Vita-Salute San Raffaele University, Milan, Italy; 4Neuroimaging research Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; 5 Department of Rehabilitation and Functional Recovery, IRCCS San Raffaele Scientific Institute, Milan, Italy; 6 Memory disorders Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy Background: Cognitive reserve (CR) delays cognitive decline due to neurodegeneration. Heterogeneous evidence suggests that education may act as CR in Dementia with Lewy Bodies (DLB). No data are currently available on the role of occupation levels as proxy of CR in this pathology. We studied brain metabolic connectivity, as assessed by FDG-PET, to shed light on the effect of education, occupation and specific occupational profiles in DLB. Methods: 33 patients with probable DLB were retrospectively included. We sorted occupation levels according to a 6-point scale and specific occupational profiles merging from the O*Net network database. We used education, occupation levels and occupational profiles as CR proxies to reveals the modulation of brain reserve (BR) as assessed with FDG-PET measures. We performed seed-based interregional correlation analyses to explore the relationship between brain metabolic connectivity and the different proxies of CR in the resting-state networks. Changes in resting-state networks connectivity among sub-groups were assessed with the Jaccard similarity coefficient. Results: Education modulates executive
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