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OECD and GRID Applications: Neuroinformatics and GRID Francesco Beltrame OECD and GRID Applications: Neuroinformatics and GRID Francesco.

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Presentazione sul tema: "OECD and GRID Applications: Neuroinformatics and GRID Francesco Beltrame OECD and GRID Applications: Neuroinformatics and GRID Francesco."— Transcript della presentazione:

1 OECD and GRID Applications: Neuroinformatics and GRID Francesco Beltrame OECD and GRID Applications: Neuroinformatics and GRID Francesco Beltrame

2 OECD Global Science Forum Neuroinformatics Working Group Final Report at : OECD Global Science Forum Neuroinformatics Working Group Final Report at :

3 Australia Gary F. Egan PhD MBA Belgium Erik De Schutter, MD PhD Czech Republic Prof. Dr. Mirko Novak DenmarkProfessor Lars Arendt-Nielsen ECLine Matthiessen, MD. PhD FinlandProfessor Mikko Sams Dr. Pentti Pulkkinen GermanyProf. Dr K. -P. Hoffmann Prof. Dr. A. Herz Greece**George K. Kostopoulos MD, PhD ItalyProf. Francesco Beltrame IndiaV. Ravindranath, Ph.D. JapanShun-ichi Amari Shiro Usui, Ph.D KoreaDr. Soo-Young Lee NetherlandsDr. Jaap van Pelt NorwayJan G. Bjaalie M.D., Ph.D. PolandAndrzej Wrobel, Ph.D., D.Sc. PortugalProf. Fernando Mira da Silva SpainCarmen Gonzalez SwedenProfessor Sten Grillner SwitzerlandDr. Paul Verschure TurkeyTurgay Dalkara UKDr Rob Bennett USAStephen H. Koslow, Ph.D. Perry L. Miller, M.D., Ph.D. Shankar Subramaniam Arthur W. Toga OECD GSF- NI Working Group

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5 Adult Human Brain 100 Billion Nerve Cells Million Billion Connections 2 Million Miles of “Wires” 1.5 Liters in Volume 3.3 Pounds in Weight 10 Watts of Energy 100 Billion Nerve Cells Million Billion Connections 2 Million Miles of “Wires” 1.5 Liters in Volume 3.3 Pounds in Weight 10 Watts of Energy

6 What? Where? How? NormalDisease Man Animal How Does the Brain Work? Cognitive Neuropsychology Computer Models of Brain Functional Anatomy (PET) Electrophysiology (EEG, ERP, MEG) In Vivo Anatomy (Ct, MRI) Tissue Autoradiography Voltage Sensitive Dyes Single Unit Recording in Vivo Anatomical Mapping Techniques (HRP, AA) Tissue Biochemistry & Pharmacology Molecular Biology & Genetics Molecule

7 Informatics Solutions & Capabilities Access Retrieval Visualization Analysis Integration Sharing Electronic Collaboration

8 The OECD GSF-NI Working Group focused on a concrete tool: the development of a NEUROINFORMATICS PORTAL (NP) Initiate an effort to develop local dissemination media using the web. Support would be sought from the respective governments to facilitate the development of such web pages that in turn could serve as prototypes for the NP. Three options were considered: government support, industry support, and e-commerce. Perhaps a combination of these resources in incremental stages. The portal is the best and current technology as the way to access and use information GRID technology is under consideration for NP Initiate an effort to develop local dissemination media using the web. Support would be sought from the respective governments to facilitate the development of such web pages that in turn could serve as prototypes for the NP. Three options were considered: government support, industry support, and e-commerce. Perhaps a combination of these resources in incremental stages. The portal is the best and current technology as the way to access and use information GRID technology is under consideration for NP

9 Governments (healthcare, industries) Citizens Internet environment Scientists in Neuroscience and IT Knowledge Repository structured knowledge area Neuroinformatics Portal it gathers applications and data it provides e-services for the three user types and their links source site 1 source site 2 source site n

10 A pilot project for the OECD Neuroinformatics Portal has been developed under Germany Governmental support, at Humboldt University in Berlin

11 The development of the Italian Node for the OECD Neuroinformatics Portal has been supported by the Italian Ministry of Education, University and Research through a FIRB grant (510 keuro) awarded to the University of Genoa and Milan (also partners of a larger FIRB-GRID project with the application Neurogrid) A Memorandum of Understanding (MoU) for the Neuroinformatics Facility will be submitted to the next OECD Ministerial Meeting in 2004 Italian GRID based Neuroinformatics node for NP

12 Complex scientific problems currently at the research frontier can be tackled by using the aforementioned Neuroinformatics Portal based on GRID technology, such as mathematical modeling of functional processes, genic- metabolic and cellular complex modeling, up to Consciousness Simulation GRID potential Neuroinformatics application areas

13 Each conscious event is a process that leaves its trace in the brain structure The Enlarged Mind Theory: the conscious mind as a LARGE collection of nested intentional relations

14 Ontogenetic Module (thalamus like) Phylogenetic Module (amygdala like) External events Categories Module (CM) (cortex like)

15 Ontogenetic Module (thalamus like) Phylogenetic Module (amygdala like) External events Categories Module (CM) (cortex like) GRID based simulation of cortical intentional relations

16 Huge storage capacity Parallel fast cross correlation between stored patterns (case based) Dedicated Intranet with GRID technology Possibly shared repository of stored intentional relations between equivalent agents Requirements for GRID implementation

17 minuti IMMAGINI PARAMETRICHE IMAGING DI PROCESSI FISIOLOGICI CEREBRALI CON TOMOGRAFIA AD EMISSIONE DI POSITRONI

18 OBIETTIVO OBIETTIVO : è necessario un modello che descrivendo la cinetica del tracciante permetta la stima delle grandezze di interesse Modelli di sistema Modelli ingresso uscita (deconvoluzione) misura di flusso ematico metabolismo concentrazioni di recettori potenziali di legame ecc... messa a punto di indici per la selezione, ottimizzazione e valutazione della terapia; sviluppare nuovi farmaci e valutarne l’efficacia

19  Lo studio del sistema fisiologico è spesso fatto utilizzando dati provenienti da regioni di interesse (ROI) e approcci di identificazione di tipo fisheriano  L’informazione estratta permette di misurare in vivo nelle ROI selezionate processi fisiologici e biochimici fondamentali per discriminarne lo stato fisiopatologico, es. depressione, Alzheimer, Parkinson  La bontà dell’imaging quantitativo dipende dalla “bontà” del modello oltreché dalla qualità del tracciante e tomografo usato QUANTIFICAZIONE A LIVELLO DI REGIONE DI INTERESSE ROI = media dei pixel delimitati Time (min) ROI

20 MODELLO FISIOLOGICO QUANTIFICAZIONE PIXEL BY PIXEL  L’informazione estratta permette di misurare in vivo a livello di singola unità componente l’immagine i processi fisiologici e biochimici.  La bontà dell’imaging quantitativo dipende dalle tecniche adottate per aumentare il rapporto segnale/disturbo  E’ un processo computazionalmente molto oneroso, es. l’analisi quantitativa di un singolo piano richiede ~2 giorni VANTAGGI : PROBLEMI: planes: 61 Images: 128x128 voxel IMMAGINE PARAMETRICA 128x128 pixel

21 IMAGING DI PROCESSI FISIOLOGICI CEREBRALI CON RISONANZA MAGNETICA - Solo alcuni parametri fisiologici -Mancanza di segnale utile in alcune regioni cerebrali (es. temporali inferiori) -Aspetti computazionali onerosi come nell’imaging PET flusso ematico volume ematico velocità consumo ossigeno PROBLEMI: - No radiazioni ionizzanti - Minori costi rispetto alla tecnica PET - No limiti per prove ripetute sullo stesso soggetto VANTAGGI: Flusso ematico Volume ematico


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