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Data Science and Neuroinformatics

INCF

Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools.

 

INCF Assembly 2022 - Day 1 Sessions

INCF

Sessions from the INCF Neuroinformatics Assembly 2022 day 1. 

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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.

 

Bayesian Models of Learning and Integration of Neuroimaging Data

Krembil Centre for Neuroinformatics

Bayesian inference (using prior knowledge to generate more accurate predictions about future events or outcomes) has become increasingly applied to the fields of neuroscience and neuroinformatics. In this course, participants are taught how Bayesian statistics may be used to build cognitive models of processes like learning or perception. This course also offers theoretical and practical instruction on dynamic causal modeling as applied to fMRI and EEG data.

 

Notebooks

Notebook systems are proving invaluable to skill acquisition, research documentation, publication, and reproducibility.  This series of presentations introduces the most popular platform for computational notebooks, Project Jupyter, as well as other resources like Binder and NeuroLibre. 

 
INCF TrainingSpace

Preprocessing Data in EEGLAB

Swartz Center for Computational Neuroscience

EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data.

 

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.

 

Neuro Ethics Day at the NeuroSchool of Aix Marseille University

NeuroSchool of Aix Marseille University

This introductory-level course provide learners with an introduction to the field of neuroethics and spans the ethics of neuroscience to the neuroscience of ethics. The ethics of neuroscience lectures cover the ethical issues that arise in device/drug enhancement, imaging/monitoring, and social uses of neuroscience in the legal/justice system. The neuroscience of ethics lectures cover the origin of ethics (neural mechanisms and evolutionary origin).

 

International Neuroethics Society Webinar Series

International Neuroethics Society

This course consists of a series of webinars organized by the International Neuroethics Society on various neuroethics topics. 

 

INCF Assembly 2022 - Training Day 1

INCF

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

 
INCF TrainingSpace

Building the Brain

INCF

Most approaches within computational neuroscience simulate systems, brain networks, local circuits, as they are now. In recent years, homeostatic regulation has been characterized and modeled; however, for understanding diseases that have their origin in genetic defects that emerge at later age, it is important to understand how these defects interact with developmental processes that occur earlier and last longer that the typical period considered for homeostatic studies.

 

The Virtual Brain Node #10 Workshop: Personalized Multi-Scale Brain Simulation

The Virtual Brain

This workshop provides basic knowledge on personalized brain network modeling using the open-source simulation platform The Virtual Brain (TVB). Participants will gain theoretical knowledge and apply this knowledge to construct brain models, process multimodal neuroimaging data for reconstructing individual brains, run simulations, and use supporting neuroinformatics tools such as collaboratories, pipelines, workflows, and data repositories.

 
INCF TrainingSpace

Session 9: Event Annotation in Neuroimaging Using HED: From Experiment to Analysis

INCF

This workshop delves into the need for, structure of, tools for, and use of hierarchical event descriptor (HED) annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. HED are a controlled vocabulary of terms describing events in a machine-actionable form so that algorithms can use the information without manual recoding.

 

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. 

 

INCF Assembly 2022 - Day 2 Sessions

INCF

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

VIEW THE PROGRAM

 

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.

 
INCF TrainingSpace

INCF Assembly 2023 - Lightning Talks (Day 1)

INCF

This course consists of three lessons, each corresponding to a lightning talk given at the first day of INCF's Neuroinformatics Assembly 2023. By following along these brief talks, you will hear about topics such as open source tools for computer vision, tools for the integration of various MRI dataset formats, as well as international data governance. 

 
INCF TrainingSpace

Session 7: Practical Guide to Overcome the Reproducibility Crisis in Small Animal Neuroimaging: Workflows, Tools, and Repositories

INCF

The workshop will include interactive seminars given by selected experts in the field covering all aspects of (FAIR) small animal MRI data acquisition, analysis, and sharing. The seminars will be followed by hands-on training where participants will perform use case scenarios using software established by the organizers. This will include an introduction to the basics of using command line interfaces, Python installation, working with Docker/Singularity containers, Datalad/Git, and BIDS.

 

Ethics and Governance

Ethical conduct of science, good governance of data, and accelerated translation to the clinic are key to high-calibre open neuroscience.  Everyday practitioners of science must be sensitized to a range of ethical considerations in their research, some having especially to do with open data-sharing. The lessons included in this course introduce a number of these topics and end with concrete guidance for participant consent and de-identification of data.

 

Bayesian Models of Learning and Integration of Neuroimaging Data

Krembil Centre for Neuroinformatics

Bayesian inference (using prior knowledge to generate more accurate predictions about future events or outcomes) has become increasingly applied to the fields of neuroscience and neuroinformatics. In this course, participants are taught how Bayesian statistics may be used to build cognitive models of processes like learning or perception. This course also offers theoretical and practical instruction on dynamic causal modeling as applied to fMRI and EEG data.