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Neuroimaging Connectomics

Krembil Centre for Neuroinformatics

This course consists of one lesson and one tutorial, focusing on the neural connectivity measures derived from neuroimaging, specifically from methods like functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI). Additional tools such as tractography and parcellation are discussed in the context of brain connectivity and mental health. The tutorial leads participants through the computation of brain connectomes from fMRI data. 

 

Simulating Brain Microcircuit Activity and Signals in Mental Health

Krembil Centre for Neuroinformatics

This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on tutorial on how to simulate, statistically evaluate, and visualize such signals. Participants will learn the simulation of signals at different spatial scales, including single-cell (neuronal spiking) and global (EEG), and how these may serve as biomarkers in the evaluation of mental health data.

 

Fundamental Methods for Single-Cell Transcriptome Analysis

Krembil Centre for Neuroinformatics

This course, consisting of one lecture and two workshops, is presented by the Computational Genomics Lab at the Centre for Addiction and Mental Health and University of Toronto. The lecture deals with single-cell and bulk level transciptomics, while the two hands-on workshops introduce users to transcriptomic data types (e.g., RNAseq) and how to perform analyses in specific use cases (e.g., cellular changes in major depression). 

 

Fundamental Methods for Genomic Analysis

Krembil Centre for Neuroinformatics

This course includes both lectures and tutorials around the management and analysis of genomic data in clinical research and care. Participants are led through the basics of genome-wide association studies (GWAS), genotypes, and polygenic risk scores, as well as novel concepts and tools for more sophisticated consideration of population stratification in GWAS.

 

Digital Health for Mental Health

Krembil Centre for Neuroinformatics

As technological improvements continue to facilitate innovations in the mental health space, researchers and clinicians are faced with novel opportunities and challenges regarding study design, diagnoses, treatments, and follow-up care. This course includes a lecture outlining these new developments, as well as a workshop which introduces users to Synapse, an open-source platform for collaborative data analysis. 

 

Applied Ethics in Machine Learning and Mental Health

Krembil Centre for Neuroinformatics

This course tackles the issue of maintaining ethical research and healthcare practices in the age of increasingly powerful technological tools like machine learning and artificial intelligence. While there is great potential for innovation and improvement in the clinical space thanks to AI development, lecturers in this course advocate for a greater emphasis on human-centric care, calling for algorithm design which takes the full intersectionality of individuals into account.

 

Reproducible Science (Including Git, Docker, and Binder)

Krembil Centre for Neuroinformatics

This course consists of two workshops which focus on the need for reproducibility in science, particularly under the umbrella roadmap of FAIR scienctific principles. The tutorials also provide an introduction to some of the most commonly used open-source scientific tools, including Git, GitHub, Google Colab, Binder, Docker, and the programming languages Python and R. 

 

The Future of Medical Data Sharing in Clinical Neurosciences

EBRAINS

This workshop hosted by HBP, EBRAINS, and the European Academy of Neurology (EAN) aimed to identify and openly discuss all issues and challenges associated with data sharing in Europe: from ethics to data safety and privacy including those specific to data federation such as the development and validation of federated algorithms. 

 

 

Current Methods in Neurotechnology

NeurotechEU

The lecture series focuses on current trends in modern techniques in neuroscience. Inspiring scientists from the NeurotechEU Alliance will give an overview of the latest advances and developments.

 

Module 5: Calcium Imaging

Mike X. Cohen

In this course, you will learn about working with calcium-imaging data, including image processing to remove background "blur", identifying cells based on threshold spatial contiguity, time-series filtering, and principal component analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

 

Module 4: fMRI

Mike X. Cohen

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

 

Module 3: Computational Models

Mike X. Cohen

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and F-I curves. The MATLAB code introduces live scripts and functions.

 

Module 2: EEG

Mike X. Cohen

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

 

Module 1: Spikes

Mike X. Cohen

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

 

Enabling Multi-Scale Data Integration: Turning Data to Knowledge

NFDI Neuroscience

This workshop is organized by the German National Research Data Infrastructure Initiative Neuroscience (NFDI-Neuro). The initiative is community driven and comprises around 50 contributing national partners and collaborators. NFDI-Neuro partners with EBRAINS AISB, the coordinating entity of the EU Human Brain Project and the EBRAINS infrastructure. We will introduce common methods that enable digital reproducible neuroscience.

 

INCF Assembly 2022 - Training Day 2

INCF

This course contains sessions from the second day of INCF's Neuroinformatics Assembly 2022.

 

INCF Assembly 2022 - Training Day 1

INCF

This course contains sessions from the first day of INCF's Neuroinformatics Assembly 2022.

 

INCF Assembly 2022 - Day 3 Sessions

INCF

Sessions from the INCF Neuroinformatics Assembly 2022 Day 3. 

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INCF Assembly 2022 - Day 2 Sessions

INCF

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

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INCF Assembly 2022 - Day 1 Sessions

INCF

Sessions from the INCF Neuroinformatics Assembly 2022 day 1. 

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