SP8 MEDICAL INFORMATICS PLATFORM

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1 SP8 MEDICAL INFORMATICS PLATFORM

2 SP8 SP8 SP11 SP12 SP6 MEDICAL INFORMATICS PLATFORM SP5 SP2 SP8 INTERACTS DIRECTLY WITH SEVERAL OTHER SUBPROJECTS Subproject 1 SP1 Strategic Mouse Brain Data Subproject 2 SP2 Strategic Human Brain Data Subproject 3 SP3 Cognitive Architectures Subproject 4 SP4 Theoretical Neuroscience Subproject 5 SP5 Neuroinformatics Platform Subproject 6 SP6 Brain Simulation Platform Subproject 7 SP7 High Performance Computing Platform Subproject 8 SP8 Medical Informatics Platform Subproject 9 SP9 Neuromorphic Computing Platform Subproject 10 SP10 Neurorobotics Platform Subproject 11 SP11 Applications Subproject 12 SP12 Ethics and Society The Human Brain Project (HBP) is a European Commission Future and Emerging Technologies Flagship that aims to achieve a multi-level, integrated understanding of brain structure and function through the development and use of information and communication technologies (ICT). These technologies will enable large-scale collaboration and data sharing, reconstruction of the brain at different biological scales, federated analysis of clinical data to map diseases of the brain, and the development of brain-inspired computing systems. 3

3 The HBP is working to achieve an integrated, multilevel understanding of brain structure and function through the development and use of information and communication technologies (ICT) The HBP s ICT platforms will allow neuroscientists, clinical researchers and information technology developers to perform diverse experiments and share knowledge, with a common goal of unlocking the most complex structure in the known universe. During the first two-and-a-half years (the Ramp-Up Phase), the HBP will collect strategic data, develop theoretical frameworks and perform the development work necessary to make the six ICT Platforms available for use by the scientific community in the Operational Phase. The HBP s ICT Platforms are: 4 Neuroinformatics (a data repository, including brain atlases) Brain Simulation (building ICT models and simulations of brains and brain components) Medical Informatics (bringing together information on brain diseases) Neuromorphic Computing (ICT that mimics the functioning of the brain) Neurorobotics (testing brain models and simulations in virtual environments) High Performance Computing (this platform will support the other Platforms) The HBP was launched in 2013 and brings together more than 100 academic and corporate Partners in more than 20 countries. HBP research is organised into twelve Subprojects (SP), each broken down into Work Packages (WP) and Tasks (T), with well-defined goals and milestones. Six Subprojects are building the ICT Platforms, while the other six are gathering data, clarifying theory and controlling ethical aspects. An additional Subproject manages and coordinates the HBP. With an unprecedented cross-disciplinary scope, the HBP s goal is to catalyse a global collaborative effort to understand the human brain and its diseases and, ultimately, to emulate its computational capabilities. Operational Objectives The Medical Informatics Platform has three operational objectives for the Ramp-Up Phase. The first is to build the tools to federate clinical data, including genetics and imaging, currently locked in hospital and research archives, while providing technical guarantees that researchers cannot link the data to individual patients except under strict medical control and legal supervision. The second is to recruit hospitals to use the system. The third is to develop tools making it possible to extract unique biological signatures of disease from multi-level data for patients with many different disorders, making it possible to develop a new, comprehensive classification of brain diseases, based on parameterised combinations of biological features and markers. This requires the development of a new class of data classification techniques called rule-based clustering. Success would accelerate the development of a new category of biologically-based diagnostics, supported by strong mechanistic hypotheses of disease causation. Disease signatures also serve as disease-specific configurations for reconstructing models of disease and performing in silico experiments with the Brain Simulation Platform. The results will help researchers to develop new targets and new strategies for treatment. Brain simulation will make it possible to predict the desirable and adverse effects of treatments, providing valuable input for industry decision-makers before they invest in expensive programmes of animal experimentation or human trials. Classification techniques created within SP8 are complemented by new methods proposed by projects selected via a competitive call. 5

4 Methodology The Medical Informatics Platform builds on existing international data generation initiatives, and allows researchers to query and analyse large volumes of clinical and other data stored on hospital and laboratory servers. The work required to build and manage the Platform can be summarised as follows. Federated data management. The Medical Informatics Platform provides a software framework allowing researchers to query clinical data stored on hospital and laboratory servers, without moving the data from the servers where it resides (in situ querying) and without compromising patient privacy. The Project is ensuring a well-balanced mixture between the solutions already existing in hospital, thirdparty infrastructure providers and its own advanced methodology. From a system point of view, this calls for a distributed hardware Platform. The Platform consists of a federated network of HBP local servers (HLS) located on hospital premises and controlled by hospital management. HLS do not themselves store hospital data. Instead, the hospital must explicitly register the data it is willing to share on the HLS itself, allowing the HLS to access the raw data stored on files exported by Hospital Information Systems in their own native formats. The preparation, deployment and support of the HBP Local Servers is the object of a subcontract to a commercial company. HLS has the same security as all other hospital servers. It is the responsibility of the hospital to configure and maintain firewalls, access control etc., using the same measures it already uses to protect hospital data and computing systems. Each HLS has a single point of access through which it participates in the federated infrastructure. All communication between HLS and the central server is securely encrypted. All responses to queries pass through an anonymisation filter, preventing transmission of any information that could be used to identify a patient. Anonymised encrypted summaries/aggregates of hospital data are stored on a central HBP server. The anonymised and encrypted summaries are used to route queries to the HLS (i.e., to determine which hospitals have the data needed to answer the queries). The central server talks with the HLS over an encrypted channel (e.g., the Secure Socket Layer, commonly used in e-banking). Encryption keys are considerably longer than those used in normal commercial applications. When a user wants to send a query to the central server she again uses an encrypted channel. Based on the encrypted summary of the hospital data the servers decides to which HLS it will forward the query. The HLS computes the result and returns the anonymised results. Software on the user s own machine collates the results. Data acquisition and integration. The HBP has already recruited its target number of hospitals, research labs, industrial companies and other large-scale data gathering initiatives (e.g. large longitudinal studies) to make their data available through the Platform. The Project s clinical outreach effort is expected to attract new hospitals to the network working towards the Project s longer-term targets for the Operational Phase. As part of this work, the Project is developing common protocols for data capture. Additionally tools and workflows necessary to appropriately anonymise all data are being provided to the hospitals to ensure privacy. A first framework is being developed at the Centre Hospitalier Universitaire Vaudois (CHUV) in Lausanne. Ultimately, this framework will be packaged and proposed for deployment in the other hospitals. The design of the technology incorporated in the Medical Informatics Platform (web-based data query, analysis, and visualisation) ensures that end-users with a clinical (hospitals), basic science, epidemiology, or pharmacology profile (to name a few) who contribute data to the Platform have free access to data, analytical models and computer-based diagnosis tools. Contact with physicians in major research hospitals suggests that the possibility of pooling clinical data is a major incentive to contribute data to the Platform. Hospitals will be able to use the tools provided by the Platform, not only for research into neurological and psychiatric disease but also as a tool they can use in other areas of medicine (e.g. oncology, cardiology etc.). This creates an additional incentive to use the Platform. Medical intelligence tools. The HBP is building software tools and techniques for data analysis, making it easier for researchers to analyse data made available through the Platform. These include machine learning tools, Bayesian model selection techniques and high dimensional data mining algorithms. A key feature of these tools and algorithms is that they can be run on massively parallel systems, speeding up the analysis. The tools and techniques made available by the Project will be used to study clinical and experimental data for the full range of brain disease, and to detect recurrent patterns (biological signatures). There are many specific analysis and visualisation tools already available freely on the web. Until the HBP has developed its own tools, it will use these existing routines, avoiding duplication and gaining time. 6 7

5 Building and operating the Platform. Ultimately, the Medical Informatics Platform will offer researchers with interactive web-based tools to contribute data to the Platform and to analyse and exploit the data it provides. Researchers using the Platform will receive training, technical support and documentation from a specially-created Service Centre. Ethical and regulatory issues. In the long run, the HBP will lead to new techniques for the early diagnosis of neurological and psychiatric disease, and for personalised treatment (predictions of response to treatment, reduction of adverse drug reactions etc.), which the Medical Informatics Platform will make available to the research community and ultimately to clinicians. The new techniques raise immediate and longer-term legal, ethical and social issues. Issues of immediate concern include questions related to the use of anonymised data, data protection and informed consent. Equally important are longer term issues of therapeutic equity. The Task Evaluation of PCP phases is working with the community and the HBP Foresight Lab to analyse these issues, formulating guidelines for ethically responsible clinical decision-making to guide the implementation and operation of the Platform. Community building and clinical outreach. The Task, The Medical Informatics Platform Service Centre is disseminating knowledge about the Medical Informatics Platform and its clinical rationale to the clinical community (psychiatrists and neurologists, clinical researchers) using lectures, presentations in conferences and workshops, summer courses teaching sessions etc. This Task is also making a special effort towards hospital managers and CIOs with particular responsibilities for privacy and data safety. As soon as the internal release of the Platform is available, the Task will incorporate demonstrations of the Platform in its regular activities. The Task leader is an active member of several neurological societies and chair of the Medical Committee of Science Europe. The planned activities will thus reach a broad audience. In parallel with this effort the Project will build relationships with the pharmaceutical industry, with the initial goal of obtaining data from clinical trials, and the longer-term objective of involving industry more closely in the work of the Project. 8 9

6 Computer Resources Platform s users Platform s Web Portal Clinical & biological Data Visualization Analysis Diagnostic Web portal: The web portal requires three state-of-the-art webservers, one for the development, one for production and one as backup (to guarantee 24/7 availability). Each of the servers needs 128GB main memory and for the disk 10TB RAID-1 and 4TB RAID-0. The server used for the web portal also serves as the central server of the federated database. Development will determine whether a medical diagnostics server separate from the data analysis and research server will be required in due course. Data Mining/Analysis: the data mining part of this Platform is extremely CPU-intensive. Consequently, access to a massively parallel system is needed, either to a massive scale cluster (EPFL s Aries, Antares or Jupiter cluster) or ideally to the BlueGene/Q via CADMOS. For the development and testing of the data mining tools, an additional server with a total of 8-16 cores, 128 gigabytes (GB) of memory and 10 terabytes (TB) of disk are needed. Development: for development and testing of all components of the Platform (web portal, federated database, data mining), state-of-the-art desktop computers/laptops are required. Federated Database: all participating centres contribute a server to interface with their databases wherever they reside. The hospitals can decide which resources they are willing to contribute. Ideally a server contributed by the hospitals will feature 64GB of main memory, 10TB of disk and are connected to the Internet with a T1 line. The resources contributed by the hospitals are not part of the Platform s computing resources. Communications are conducted via the public Internet. Communications links are protected by end-to-end encryption guaranteeing effective protection of data passing across the network. Users will access the Platform via a single point of access (a web portal) shared among all the Platforms. The services themselves will be based on the second level web portal described above. Neuroscientists R&D Pharma Clinicians User s Queries MetaData Queries forwarded to hospitals Hospitals Server Anonymized Hospitals dataserver Anonymized Hospitals data Server Anonymized data Signature of brain diseases Open databases Public databases Research data HBP brain models Data Mining Servers Data Mining Servers Data Mining Servers Data Mining Servers Rule-based Algorithms Architectural view of the Medical Informatics Platform 10 11

7 Deliverables MONTH 6 REPORT MEDICAL INFORMATICS PLATFORM V1 - SPECIFICATION DOCUMENT This report provides a detailed specification of the Medical Informatics Platform v1 including the medical intelligence tools. An annex will provide a description of other work preparing for later versions of the Platform. The report will specify indicators of progress and target values for the indicators. MONTH 12 REPORT MEDICAL INFORMATICS PLATFORM V1 SET-UP DOCUMENT This report describes progress in the development of the Platform, recruitment of hospitals and development of the medical intelligence tools. MONTH 18 PROTOTYPE MEDICAL INFORMATICS PLATFORM V1 PRELIMINARY RELEASE FOR INTERNAL CONSORTIUM USE This will be the first release of the Platform, which will be available for use by members of the HBP Consortium. The Platform will allow researchers to access anonymised clinical data (imaging data, blood chemistry data etc.) provided by the organizations (hospitals, pharmaceutical companies) where Local Hospital Servers have been installed and to analyse the data using early versions of the Medical Intelligence Tools. MONTH 30 PROTOTYPE MEDICAL INFORMATICS PLATFORM V1 This will be the first official release of the Medical Informatics Platform. The Platform will allow researchers from the clinical and pharmacological research community to access anonymised clinical data (imaging data, blood chemistry data etc.) provided by the organizations (hospitals, pharmaceutical companies) where Local Hospital Servers have been installed and to analyse the data using the first fully tested versions of the Medical Intelligence Tools. MONTH 30 REPORT MEDICAL INFORMATICS PLATFORM V1 DOCUMENTATION The prototype will be accompanied by technical and user documentation facilitating use of the Platform by users within and outside the HBP Consortium and by a roadmap describing plans for future development in the Operational Phase of the Project. This Deliverable will describe the assessment of the third phase of the Pre- Commercial Procurement process

8 Milestones MONTH 6 Specification of requirements for data federation and anonymisation tools, requirements for web based user services Recruitment of hospitals and other data sources in progress Algorithms for deriving disease signatures, mechanisms and milestones: identification and preliminary evaluation of clustering/data mining algorithms Medical Informatics Platform fully specified Requirements for user documentation and support for Medical Informatics Platform; guidelines for establishing alliances MONTH 12 Complete concept for data federation and anonymisation tools Identification of data mining algorithms for identification of disease signatures, final parallelisation concept for use on supercomputers/clusters Identification of potential medical informatics alliances Prototype for t-sne and t-sne + GENESPACE in a representative large sample Algorithms identified and specifications written MONTH 18 Prototype of data federation and anonymisation tools Federation agreements with at least five hospitals, research initiatives, companies First prototype data mining tools Medical Informatics Platform ready for internal release Medical Informatics Platform starts user support and training, guidebook for the use of the Medical Informatics Platform - initial release Prototype for both algorithms in a distributed framework Prototypes of algorithms for learning models from multi-target data streams, prototypes of algorithms for structured output prediction, prototypes of algorithms for feature ranking, prototype algorithm for subgroup discovery from multi-resolution data, prototype algorithm for subgroup discovery from heterogeneous data ViperCharts evaluation engine MONTH 24 Data federation tools scaled up for full-scale operations, prototypes and anonymisation procedures tested Testing of data mining tools completed Rule-based hypotheses of possible links between genetic signatures of disease Tools testing completed and reports on algorithms MONTH 30 Final prototypes of data federation and anonymisation tools At least five hospitals, research initiatives, companies contributing to/using Platform Second prototype of data mining tools Medical Informatics Platform ready for community release Medical Informatics Platform ready for community release. Data federation tools scaled up for full-scale operations; prototypes, tools and anonymisation procedures tested. Medical Informatics Platform ready for internal release Data federation, anonymisation and data mining tools fully specified Medical Informatics Platform fully specified. First hospitals recruited. M 6 Guidebook for the use of the Medical Informatics Platform Validated algorithms for heterogeneous data, integrated into the HBP data analytics platform Tools deployed in the Medical Informatics Platform M 12 M 18 M 24 M 30 M 6 M 12 M 18 M 24 M

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10 Partners ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών Canada P44 Royal Institution for the Advancement of learning McGill University MCGILL France P76 Université Victor Segalen Bordeaux II UB2 Greece P3 Athens University of Economics and Business AUEB P37 National and Kapodistrian University of Athens UoA Israel P55 Tel Aviv University TAU Netherlands P81 Academisch Ziekenhuis Leiden Leids Universitair Medisch Centrum LUMC Slovenia P87 Institut Jozef Stefan JSI Sweden P77 Uppsala Universitet UU Switzerland P1 Ecole Polytechnique Fédérale de Lausanne EPFL P23 Centre Hospitalier Universitaire Vaudois CHUV P113 Hopitaux Universitaires de Genève HUG United Kingdom P70 University College London UCL United States P74 University of Southern California USC 19

11 Work Packages and Key Personnel Subproject Co-Leaders Richard FRACKOWIAK, Anastasia AILAMAKI SP8 Objective To build the tools to federate clinical data, to recruit hospitals to use the system, and to develop tools to extract unique biological signatures of diseases, making it possible to develop a new, comprehensive classification of brain diseases. WP8.1 Tools for Federated Data Management Anastasia AILAMAKI - EPFL WP8.4 Medical Informatics Platform: integration and operations Richard FRACKOWIAK - CHUV In-situ distributed database querying Data provenance, preservation and integration Workflow infrastructure Data privacy Anastasia AILAMAKI - EPFL Vasilis VASSALOS - AUEB Yannis IOANNIDIS - UOA Anastasia AILAMAKI - EPFL WP8.2 Data acquisition and federation Ferath KHERIF - CHUV Use of historical longitudinal databases for epidemiology and proof of principle studies in neurodegenerative disease Enabling the acquisition of data from prospective studies Ferath KHERIF - CHUV Art TOGA - USC Bogdan DRAGANSKI - CHUV Ferath KHERIF - CHUV WP8.3 Medical intelligence tools for data categorisation John ASHBURNER - UCL Novel mathematical tools for the characterisation and classification of neurological and psychiatric disease Advanced topographical methods for brain referencing and normalisation Linking t-sne maps and GENESPACE clusterings from the ABA Scalable algorithms for rule-based clustering of heterogeneous data Mira MARCUS-KALISH - TAU Yoav BENJAMINI - TAU John ASHBURNER - UCL Boudewijn LELIEVELDT - LUMC Saso DZEROSKI - JSI WP8.5 Integration, website construction, maintenance and administration Setting up federated infrastructure Medical Informatics Platform: user support and community building Ethical and regulatory issues Clinical trials service centre The Medical Informatics Platform Service Centre Richard FRACKOWIAK - CHUV Richard FRACKOWIAK - CHUV Richard FRACKOWIAK - CHUV Kathinka EVERS - UU Jean-Marc ORGOGOZO - UB2 Giovanni FRISONI - HUG Alan EVANS - MCGILL WP8.6 Medical Informatics Platform: scientific coordination Richard FRACKOWIAK - CHUV Scientific coordination and support Richard FRACKOWIAK - CHUV 20 21

12 PROF. RICHARD FRACKOWIAK CHUV PROF. ANASTASIA AILAMAKI EPFL PROF. JOHN ASHBURNER UCL YOAV BENJAMINI TAU Richard Frackowiak is Professor and head of the Department of Clinical Neurosciences at the Université de Lausanne (UNIL) and its Centre Hospitalier Universitaire Vaudois (CHUV). He has an MA and MD from Cambridge, a DSc from London University, an honorary professorship from UCL and a titular chair at the Ecole Polytechnique Fédérale de Lausanne (EPFL). A Fellow of the Academies of Medical Sciences of the UK, France and Belgium, he is a member of the Academia Europaea and a foreign associate of the Institute of Medicine of the American Academies. Along with founding the Wellcome Department of Imaging Neuroscience and its Functional Imaging Laboratory in 1994, he has also served as Foundation Professor of Cognitive Neurology at University College London, Director of the Department of Cognitive Studies at the Ecole Normale Superieure in Paris, Wellcome Trust Principal Clinical Research Fellow and Vice-Provost of UCL after a period as Dean-Director of its Institute of Neurology. Frackowiak has pubished more than 400 papers. Anastasia Ailamaki is a Professor of Computer Sciences at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. She earned her Ph.D. in Computer Science from the University of Wisconsin-Madison in Her research interests are in database systems and applications, specifically in strengthening the interaction between the database software and the underlying hardware and I/O devices, and in automating database design and computational database support for scientific applications. She has received a Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), six best-paper awards at top conferences ( ), and an NSF career award (2002). Ailamaki co-directs the HBP Medical Informatics Subproject. John Ashburner is a reader at the Wellcome Trust Centre for Neuroimaging. He has a Ph.D. in Neuroscience from University College London (UK), where he held positions as a lecturer and a senior lecturer before taking his current job. Prof. Ashburner is co-author (contributed about 45,000 lines of code) for SPM a widely-used software package for modelling functional and structural neuroimaging data. He has over 14,000 citations and an H-index of 52. His research is primarily in imaging science, focusing on developing models for image registration, segmentation, computational anatomy and pattern recognition. Yoav Benjamini is currently the Nathan and Lily Professor of Applied Sciences at Tel Aviv University. His scientific work combines theoretical research in statistical methodology with applied research that involves complex problems with massive data. His methodological work is on selective and simultaneous inference (multiple comparisons), and centres on the False Discovery Rate (FDR) criterion, as well as on general methods for data analysis, data mining and data visualisation. His current interest is the replicability problem in science. Benjamini enjoys cooperating with scientists on the design and analysis of their experiments. He shares a lab with Prof. Ilan Golani for investigating the behaviour of animals when exposed a novel environment. The computational and statistical tools being developed enable them to find structure in the huge data sets being compiled in such experiments. Benjamini teaches basic and advanced statistics courses and workshops and supervises many research students

13 PROF. BOGDAN DRAGANSKI CHUV PROF. SAŠO DŽEROSKI JSI PROF. ALAN EVANS MCGILL PROF. KATHINKA EVERS UU Bogdan Draganski joined the Wellcome Trust Centre for Neuroimaging, London, UK as research fellow in Richard Frackowiak s group, after completing his clinical training in Neurology. He is currently an assistant professor at the University of Lausanne and head of the Laboratoire de recherche en neuroimagerie (LREN). Sašo Džeroski is a Scientific Councillor at the Department of Knowledge Technologies, Jožef Stefan Institute, and a professor at Jožef Stefan International Postgraduate School. He has made major contributions to the machine learning areas of relational learning, computational scientific discovery, and the emerging area of inductive databases and constraint-based data mining. Recent contributions include methods for learning from dynamic/temporal data. He has used machine learning methods for the analysis of heterogeneous data from patients with embryonal tumours, and data on how tuberculosis and Salmonella manipulate the human immune system. Alan Evans defended his Ph.D. thesis in biophysics at Leeds University in the UK. He then spent five years at Atomic Energy of Canada, working on the physics and analysis of PET images and subsequently moved to the Montreal Neurological Institute (MNI) at McGill. His research interests include multimodal brain imaging with PET and MRI, structural network modelling and large-scale brain databasing. He has published 400+ peerreviewed papers and has held various offices, most notably as director of the McConnell Brain Imaging Centre (BIC). He is a founding member of the International Consortium for Brain Mapping (ICBM), was one of the founders of the Organisation for Human Brain Mapping (OHBM), and is also a PI in the Montreal Consortium for Brain Imaging Research (MCBIR). In 2003, he received the prestigious CIHR Senior Scientist Award. Kathinka Evers, Ph.D., is a senior researcher and professor of philosophy at the Centre for Research Ethics and Bioethics (CRB) at Uppsala University. She has been an invited professor (Condorcet Chair) at the Ecole Normale Supérieure in Paris, at the Collège de France, and at the Centro de Investigaciones Filosoficas, in Buenos Aires (Argentina). Her research focuses on philosophy of mind, neurophilosophy, bioethics and neuroethics. She directs Uppsala University s teaching and research on neuroethics and established its first courses in the subject. She is also interested in the social responsibility of science, and served as the Executive Director for the Standing Committee for Ethics and Responsibility in Science of the International Council for Science. She has been an expert in scientific review panels at the ERC, covering the topic The Human Mind and Its Complexity. Kathinka Evers co-directs the HBP Ethics and Society Subproject

14 GIOVANNI B. FRISONI HUG PROF. YANNIS IOANNIDIS UoA DR. FERATH KHERIF CHUV PROF. BOUDEWIJN LELIEVELDT LUMC Giovanni B. Frisoni is the Deputy Scientific Director of the National Alzheimer s Centre in Brescia, Italy and the head of the Laboratory of Neuroimaging and Translational Care Unit. He is author of over 350 scientific papers and the editor of prominent journals on aging and neurobiology. Yannis Ioannidis is a professor in the Department of Informatics and Telecommunications at the University of Athens, and the President and General Director of the Athena Research and Innovation Center. He received his Ph.D. degree in Computer Science from the University of California, Berkeley in 1986, his MSc degree in Applied Mathematics from Harvard University in 1983, and his Diploma in Electrical Engineering from the National Technical University of Athens in His research interests include database and information systems, scientific systems and medical informatics, cloud computing, dataflow management and data analytics, social information systems, and digital libraries topics on which he has published over a 150 articles and conferences. He is an ACM and IEEE Fellow and a member of Academia Europaea, and has received several awards for his research and teaching work. He has recently been appointed as the Greek National Representative to the EU Capacities Programme on Research Infrastructures. Ferath Kherif is a senior lecturer at CHUV/ UNIL and a Principal Investigator at the Laboratoire de recherche en neuroimagerie (LREN) conducting multi-disciplinary research in two key domains: mathematical/statistical modelling and psychology of language and memory. He is a co-author of the Statistical Parametric Mapping (SPM) software, a leading software for brain imaging analysis worldwide. He has developed innovative methods for multivariate fmri analysis (Kherif et al. 2002) with successful implementation in cognitive domains (Simon et al. 2004) and clinical domains (Kawasaki et al. 2007). Before joining the LREN he carried out research on motor speech theory in Cambridge at the Cognition and Brain Sciences unit and at the Wellcome Trust Centre for Neuroimaging. Boudewijn Lelieveldt is a full professor of Biomedical Imaging at the Department of Radiology, Leiden University Medical Centre (Netherlands) where he heads the Division of Image Processing. He also has an appointment at the Pattern Recognition and Bioinformatics group, Faculty of Electrical Engineering, Mathematics and Computer Science at the Delft University of Technology. He is the PI of two grants on data analytics from the Dutch Technology Foundation, and Co-PI of the STW Perspective program IMAGENE on population imaging genetics. He has participated in several FP7 projects (ENCITE, EMIL, DIMI, BRAINPATH), developing dedicated analysis methods for the latest longitudinal small animal imaging modalities. He recently co-initiated the Genes in Space research team, which focuses on mining the Allen Brain Atlas to link transcriptomic, proteomic and in-vivo imaging data. Lelieveldt has published more than 120 papers in journals and conference proceedings

15 DR. MIRA MARCUS-KALISH TAU PROF. JEAN-MARC ORGOGOZO UB2 PROF. ARTHUR TOGA USC PROF. VASILIS VASSALOS AUEB Mira Kalish specialises in converging technologies and combined statistical and modelling analysis in Bio-Medicine. Dr. Kalish was a computer programmer and system analyser professional when she started her the academic studies. She has a BSc in Biology and Statistics from the Hebrew University, and a PhD in operation research for developing a computerised EKG system (1986). She did her post-doc at Harvard in the Dana Farber Cancer Institute. Jean-Marc Orgogozo is Professor of Neurology at the University of Bordeaux, Head of the Clinical Neurosciences Department at the University Hospital and Director of the Federation of the Bordeaux Neuroscience Institutes. He served for 20 years as an Expert in Neurology to the World Health Organization, where he was responsible for the Neurological Adaptation of the WHO International Classification of Diseases-10. He is currently Chair of the International Working Group on Harmonization for Dementia Trials. Arthur Toga is the Director of the Laboratory of Neuro Imaging in the Department of Neurology at the University of Southern California. His laboratory uses visualisation techniques and statistical measurements to study morphometric variability in humans, nonhuman primates and rodents, and is creating three-dimensional digital neuroanatomic and functional neuroanatomic atlases for stereotactic localisation and multi-subject comparison. The lab develops local deformation techniques to equate brain data sets from different modalities and different subjects, and electronic databases for the archival, interaction and distribution of brain data. Vasilis Vassalos is currently an Associate Professor at the Department of Informatics of the Athens University of Economics and Business. Prior to that, he was an Assistant Professor at the Department of Information Systems in the Stern School of Business of New York University ( ), and at AUEB ( ). He holds a Ph.D. And M.Sc. in Computer Science from Stanford University (2000 and 1998 respectively). In his career he has published more than 40 research papers in international peer-reviewed journals and conferences in the areas of databases and the Web. He holds US patents for work on information integration, and regularly serves on the program committees of the major conferences in databases, as well as a reviewer for major journals. He has led a number of publicly funded research projects in the areas of federated information processing and was a co-founder of a successful start-up company in the area of information integration (Enosys Software, founded in 2000, acquired by BEA Systems in 2003)

16 The Medical Informatics Platform is federating and mining data to identify biological signatures of brain disease SP11 SP10 SP12 SP9 SP8 SP6 SP7 SP1 SP4 SP3 SP5 The Medical Informatics Platform developed by SP8 will provide researchers inside and outside the HBP with access to anonymised patient data from a wide range of psychiatric and neurological diagnoses. Patterns in the data ( biological signatures of disease ) will lead to new classifications of brain disorders and more accurate diagnoses, paving the way for personalised medicine. Computer models of disease modified versions of the models of the healthy brain developed in SP6 will allow researchers to test their hypotheses and to simulate potential therapies, speeding up the development of new drugs. SP2 THE HBP S EMPHASIS ON COLLABORATION IS EXEMPLIFIED BY THE INTERDEPENDENCE OF ITS TWELVE RESEARCH SUBPROJECTS Subproject 1 SP1 Strategic Mouse Brain Data Subproject 2 SP2 Strategic Human Brain Data Subproject 3 SP3 Cognitive Architectures Subproject 4 SP4 Theoretical Neuroscience Subproject 5 SP5 Neuroinformatics Platform Subproject 6 SP6 Brain Simulation Platform Subproject 7 SP7 High Performance Computing Platform Subproject 8 SP8 Medical Informatics Platform Subproject 9 SP9 Neuromorphic Computing Platform Subproject 10 SP10 Neurorobotics Platform Subproject 11 SP11 Applications Subproject 12 SP12 Ethics and Society 30

17 HumanBrainProj HumanBrainProj