This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
The Virtual Brain (TVB) is an open-source, multi-scale, multi-modal brain simulation platform. In this lesson, you get introduced to brain simulation in general and to TVB in particular. This lesson also presents the newest approaches for clinical applications of TVB - that is, for stroke, epilepsy, brain tumors, and Alzheimer’s disease - and show how brain simulation can improve diagnostics, therapy, and understanding of neurological disease.
This lesson explains the mathematics of neural mass models and their integration to a coupled network. You will also learn about bifurcation analysis, an important technique in the understanding of non-linear systems and as a fundamental method in the design of brain simulations. Lastly, the application of the described mathematics is demonstrated in the exploration of brain stimulation regimes.
In this lesson, the simulation of a virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved clinical results. You will learn about the fundamentals of epilepsy, as well as the concepts underlying epilepsy simulation. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.
This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.
This lecture covers the ethical implications of the use of pharmaceuticals to enhance brain functions and was part of the Neuro Day Workshop held by the NeuroSchool of Aix Marseille University.
This lecture provides an introduction to the application of genetic testing in neurodevelopmental disorders.
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 is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes.
This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.
This is an introductory lecture on whole-brain modelling, delving into the various spatial scales of neuroscience, neural population models, and whole-brain modelling. Additionally, the clinical applications of building and testing such models are characterized.
This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling.
In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
This lecture provides an overview of successful open-access projects aimed at describing complex neuroscientific models, and makes a case for expanded use of resources in support of reproducibility and validation of models against experimental data.
This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture covers the biomedical researcher's perspective on FAIR data sharing and the importance of finding better ways to manage large datasets.