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. 

This course provides a broad, non-technical overview of the field of neurotechnology. It is intended to provide users with a fundamental understanding of how neurotechnology works.

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 module is intended to provide a foundation in energy-based models. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Introduction to Deep Learning (module 1 of the course), Parameter Sharing (module 2 of the course),

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.

This couse is the opening module for the University of Toronto's Krembil Centre for Neuroinformatics' virtual learning series Solving Problems in Mental Health Using Multi-Scale Computational Neuroscience. Lessons in this course introduce participants to the study of brain disorders, starting from elemental units like genes and neurons, eventually building up to whole-brain modelling and global activity patterns.

There is a broad consensus among researchers, publishers, and funding bodies that open sharing of data is needed to address major reproducibility and transparency challenges that currently exist in all scientific disciplines. In addition to potentially increasing the utilization of shared data through re-analysis and integration with other data, data sharing is beneficial for individual researchers through data citation and increased exposure of research.

This course consists of a three-part session from the second day of INCF's Neuroinformatics Assembly 2023. The lessons describe various on-going efforts within the fields of neuroinformatics and clinical neuroscience to adjust to the increasingly vast volumes of brain data being collected and stored.

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. 

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.

Over the last three decades, neuroimaging research has seen large strides in the scale, diversity, and complexity of studies, the open availability of data and methodological resources, the quality of instrumentation and multimodal studies, and the number of researchers and consortia. The awareness of rigor and reproducibility has increased with the advent of funding mandates, and with the work done by national and international brain initiatives.

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

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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). 

As research methods and experimental technologies become ever more sophisticated, the amount of health-related data per individual which has become accessible is vast, giving rise to a corresponding need for cross-domain data integration, whole-person modelling, and improved precision medicine. This course provides lessons describing state of the art methods and repositories, as well as a tutorial on computational methods for data integration. 

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.

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.

This course consists of a series of lessons and tutorials aimed at providing an introductory overview of data science implementation in MATLAB®, a widely used, numeric and computing platform which works with many types of data and file formats. In this course, you will learn the basic concepts behind data science in general, as well as how to apply those concepts within the MATLAB framework in particular.

This course introduces researchers to the Brain Imaging Data Structure (BIDS), the official community standard for organizing and sharing PET data. BIDS simplifies collaboration, streamlines analysis, and ensures your research remains future-proof by enabling compatibility with an ever-growing ecosystem of open datasets and community-developed tools.

The Neurodata Without Borders: Neurophysiology project (NWB:N, https://www.nwb.org/) is an effort to standardize the description and storage of neurophysiology data and metadata. NWB enables data sharing and reuse and reduces the energy barrier to applying data analytics both within and across labs. Several laboratories, including the Allen Institute for Brain Science, have wholeheartedly adopted NWB.

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.