This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.
In this lesson, users will learn how to appropriately sort and bin neural spikes, allowing for the generation of a common and powerful visualization tool in neuroscience, the histogram.
This tutorial builds on the previous lesson's demonstration of spectral analysis of one EEG channel. Here, users will learn how to compute and visualize spectral power from all EEG channels using MATLAB.
In this lesson, users will learn more about the steady-state visually evoked potential (SSEVP), as well as how to create and interpret topographical maps derived from such studies.
In the final lesson of this module, users will learn how to correlate endogenous alpha power with SSVEP amplitude from EEG data using MATLAB.
This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.
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.
This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses.
This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.
This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.
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 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.
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 tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.