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

This lesson provides an introduction to the Symposium on Science Management at the Canadian Association for Neuroscience 2019 Meeting.

This lesson gives a primer to project management in a scientific context, with a particular neuroinformatic case study. 

In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them. 

This lesson covers "Knowledge Translation", the activities involved in moving research from the laboratory, the research journal, and the academic conference into the hands of people and organizations who can put it to practical use.

In this lesson, you will hear about the various methods developed and employed in managing performance. 

This lesson provides an overview of how to manage relationships in a research context, while highlighting the need for effective communication at various levels.

In this lesson you will hear a panel discussion which hosts experts in the field whom have extensive experience with management in a science setting.

Manipulate the default connectome provided with TVB to see how structural lesions effect brain dynamics. In this hands-on session you will insert lesions into the connectome within the TVB graphical user interface (GUI). Afterwards, the modified connectome will be used for simulations and the resulting activity will be analysed using functional connectivity.

This presentation discusses the impact of data sharing in stroke.

This talks presents an overview of the potential for data federation in stroke research.

This talk focuses on the EAN Scientific Panel of Stroke, in particular on the aims and roles of the panel.

This lesson describes the fundamentals of genomics, from central dogma to design and implementation of GWAS, to the computation, analysis, and interpretation of polygenic risk scores. 

This lesson provides an overview of the database of Genotypes and Phenotypes (dbGaP), which was developed to archive and distribute the data and results from studies that have investigated the interaction of genotype and phenotype in humans.

This brief talk goes into work being done at The Alan Turing Institute to solve real-world challenges and democratize computer vision methods to support interdisciplinary and international researchers. 

This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.

The number of patients with dementia is estimated to increase given the aging population. This will lead to a number of challenges in the future in terms of diagnosis and care for patients with dementia. To meet these needs such as early diagnsosis and development of prognostic biomarkers, large datasets, such as the federated datasets on dementia. The EAN Dementia and cognitive disorders scientific panel can play an important role as coordinator and connecting panel members who wish to participate in e.g. consortia.

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices. 

In this lesson, you will learn about the connectome, the collective system of neural pathways in an organism, with a closer look at the neurons, synapses, and connections of particular species.