This lecture presents the Medical Informatics Platform's data federation in epilepsy.
Explore how to setup an epileptic seizure simulation with the TVB graphical user interface. This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.
This talk introduces data sharing initiatives in Epilepsy, particularly across Europe.
The epilepsy SP actively promotes and supports epilepsy-related issues as well as educational and scientific activities within the framework of EAN. Our partners ILAE/ILAE Europe, EpiCare, EPNS and AOAN are actively involved. One of the major tasks is promoting submissions of session proposals for EAN congress balancing new scientific approaches and educational need for teaching courses. Outside of congress activities, contributions to e-learning facilities on the EAN website such as registrars reading list, scales and scores and breaking news are regularly presented or updated. Particular since the COVID pandemic, publications on COVID and any issues of epilepsy or seizures are regularly screened and summarized in neurology updates. In partnership with the ILAE/ILAE Europe, several guidelines are under preparation.
In this lecture, the speaker demonstrates Neurokernel's module interfacing feature by using it to integrate independently developed models of olfactory and vision LPUs based upon experimentally obtained connectivity information.
In this talk the speakers will give a brief introduction of the Fenix Infrastructure and Service Offering, before focusing on Data Safety. The speaker will take the participants through the ETHZ-CSCS offering for EBRAINS and all the HBP Communities highlighting the Infrastructure role in a service implementation in respect of Security. Particular attention will be on showing what tools ETHZ-CSCS provides to a Portal/Service provider such as EBRAINS, MIP/HIP, TVB, NRP amongst others. Finally there will be given a quick glimpse into the future and the role that “multi-tenancy” will play.
Introduction to the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and go through both motivation and process involved in moving your research computing to the cloud. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
This lecture on multi-scale entropy by Jil Meier is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.
This lecture on modeling epilepsy using TVB by Julie Courtiol is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.
DAQCORD is a framework for the design, documentation and reporting of data curation methods in order to advance the scientific rigour, reproducibility and analysis of the data. This lecture covers the rationale for developing the framework, the process in which the framework was developed, and ends with a presentation of the framework. While the driving use case for DAQCORD was clinical traumatic brain injury research, the framework is applicable to clinical studies in other domains of clinical neuroscience research.
This lecture focuses on ontologies for clinical neurosciences.
This talks presents an overview of the potential for data federation in stroke research.
This lecture explains the need for data federation in medicine and how it can be achieved.
In this session the Medical Informatics Platform (MIP) federated analytics is presented. The current and future analytical tools implemented in the MIP will be detailed along with the constructs, tools, processes, and restrictions that formulate the solution provided. MIP is a platform providing advanced federated analytics for diagnosis and research in clinical neuroscience research. It is targeting clinicians, clinical scientists and clinical data scientists. It is designed to help adopt advanced analytics, explore harmonized medical data of neuroimaging, neurophysiological and medical records as well as research cohort datasets, without transferring original clinical data. It can be perceived as a virtual database that seamlessly presents aggregated data from distributed sources, provides access and analyze imaging and clinical data, securely stored in hospitals, research archives and public databases. It leverages and re-uses decentralized patient data and research cohort datasets, without transferring original data. Integrated statistical analysis tools and machine learning algorithms are exposed over harmonized, federated medical data.
The Medical Informatics Platform (MIP) is a platform providing federated analytics for diagnosis and research in clinical neuroscience research. The federated analytics is possible thanks to a distributed engine that executes computations and transfers information between the members of the federation (hospital nodes). In this talk the speaker will describe the process of designing and implementing new analytical tools, i.e. statistical and machine learning algorithms. Mr. Sakellariou will further describe the environment in which these federated algorithms run, the challenges and the available tools, the principles that guide its design and the followed general methodology for each new algorithm. One of the most important challenges which are faced is to design these tools in a way that does not compromise the privacy of the clinical data involved. The speaker will show how to address the main questions when designing such algorithms: how to decompose and distribute the computations and what kind of information to exchange between nodes, in order to comply with the privacy constraint mentioned above. Finally, also the subject of validating these federated algorithms will be briefly touched.
The Medical Informatics Platform (MIP) Dementia had been installed in several memory clinics across Europe allowing them to federate their real-world databases. Research open access databases had also been integrated such as ADNI (Alzheimer’s Dementia Neuroimaging Initiative), reaching a cumulative case load of more than 5'000 patients (major cognitive disorder due to Alzheimer’s disease, other major cognitive disorder, minor cognitive disorder, controls). The statistic and machine learning tools implemented in the MIP allowed researchers to conduct easily federated analyses among Italian memory clinics (Redolfi et al. 2020) and also across borders between the French (Lille), the Swiss (Lausanne) and the Italian (Brescia) datasets.
This lecture talks about the usage of knowledge graphs in hospitals an related challenges of semantic interoperability.
This lecture discusses risk-based anonymization approaches for medical research.
The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.