This lecture presents the Medical Informatics Platform's data federation in epilepsy.
This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient.
This talk introduces data sharing initiatives in Epilepsy, particularly across Europe.
In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works.
This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.
This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.
In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity.
This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.
This lesson corresponds to slides 1-64 in the PDF below.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.
This lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields.
This tutorial walks users through the creation and visualization of activation flat maps from fMRI datasets.
This tutorial demonstrates to users the conventional preprocessing steps when working with BOLD signal datasets from fMRI.
In this tutorial, users will learn how to create a trial-averaged BOLD response and store it in a matrix in MATLAB.
This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.
This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets.
In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.
This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging.
This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time.
This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.
In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data.