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.

In this lesson you will learn how to simulate seizure events and epilepsy in The Virtual Brain. We will look at the paper On the Nature of Seizure Dynamics, which describes a new local model called the Epileptor, and apply this same model in The Virtual Brain. This is part 1 of 2 in a series explaining how to use the Epileptor. In this part, we focus on setting up the parameters.

This lecture goes into detailed description of how to process workflows in the virtual research environment (VRE), including approaches for standardization, metadata, containerization, and constructing and maintaining scientific pipelines. 

This lesson provides an overview of how to conceptualize, design, implement, and maintain neuroscientific pipelines in via the cloud-based computational reproducibility platform Code Ocean. 

This lesson provides an overview of how to construct computational pipelines for neurophysiological data using DataJoint.

This talk describes approaches to maintaining integrated workflows and data management schema, taking advantage of the many open source, collaborative platforms already existing.

This lesson is the first of three hands-on tutorials as part of the workshop Research Workflows for Collaborative Neuroscience. This tutorial goes over how to visualize data with Scanpy, a scalable toolkit for analyzing single-cell gene expression. 

This hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines. 

In this third and final hands-on tutorial from the Research Workflows for Collaborative Neuroscience workshop, you will learn about workflow orchestration using open source tools like DataJoint and Flyte. 

This lecture provides a detailed description of how to incorporate HED annotation into your neuroimaging data pipeline. 

This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.

This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

This lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter. 

This lecture provides an general introduction to epilepsy, as well as why and how TVB can prove useful in building and testing epileptic models. 

This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.

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.

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.

This lecture provides an introduction to the application of genetic testing in neurodevelopmental disorders.

This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.