Neuroanatomy provides one of the unifying frameworks for neuroscience and thus it is not surprising that it provides the basis for many neuroinformatics tools and approaches.  Regardless of whether one is working at the subcellular, cellular or gross anatomical level or whether one is modeling circuitry, molecular pathways or function, at some point, this work will include an anatomical reference.

Sessions from the INCF Neuroinformatics Assembly 2022 Day 3. 

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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.

Given the extreme interconnectedness of the human brain, studying any one cerebral area in isolation may lead to spurious results or incomplete, if not problematic, interpretations. This course introduces participants to the various spatial scales of neuroscience and the fundamentals of whole-brain modelling, used to generate a more thorough picture of brain activity.

This lecture series is presented by NeuroTechEU, an alliance between eight European universities with the goal to build a trans-European network of excellence in brain research and technologies. By following along with this series, participants will learn about the history of cognitive science and the development of the field in a sociocultural context, as well as its trajectory into the future with the advent of artificial intelligence and neural network development.

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. 

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.

Get up to speed about the fundamental principles of full brain network modeling using the open-source neuroinformatics platform The Virtual Brain (TVB). This simulation environment enables the biologically realistic modeling of whole-brain network dynamics across different brain scales, using personalized structural connectome-based approach.

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.

This is a freely available online course on neuroscience for people with a machine learning background. The aim is to bring together these two fields that have a shared goal in understanding intelligent processes. Rather than pushing for “neuroscience-inspired” ideas in machine learning, the idea is to broaden the conceptions of both fields to incorporate elements of the other in the hope that this will lead to new, creative thinking.

There is a growing recognition and adoption of open and FAIR science practices in neuroscience research. This is predominately regarded as scientific progress and has enabled significant opportunities for large, collaborative, team science. The efforts and practical work that go into creating an open and FAIR landscape extend far beyond just the science.

This course corresponds to the second session of INCF's Neuroinformatics Assembly 2023. This series of talks continues a discussion of FAIR principles from the first session, with a greater emphasis on brain data (humans and animals) atlases for data analysis and integation. 

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). 

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.

This course outlines how versioning code, data, and analysis software is crucially important to rigorous and open neuroscience workflows that maximize reproducibility and minimize errors.Version control systems, code-capable notebooks, and virtualization containers such as Git, Jupyter, and Docker, respectively, have become essential tools in data science.

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

Most neuroscience journals request authors to make their data publicly available in appropriate repositories. The requirements and policies put forward by journals vary, and the services provided for different types of data also differ considerably across repositories.

The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods.

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.

This course consists of several lightning talks from the second day of INCF's Neuroinformatics Assembly 2023. Covering a wide range of topics, these brief talks provide snapshots of various neuroinformatic efforts such as brain-computer interface standards, dealing with multimodal animal MRI datasets, distributed data management, and several more.