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.

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 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.

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.

In this short series of lectures, participants will take a look at articles using TVB in a clinical context. Specifically, participants will see how TVB can help to predict recovery after stroke and how individual epileptic seizures are simulated. The course lecturers will briefly describe the methods used and results achieved in the articles.

This course includes two tutorials on R, a programming language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible.

Standards and best practices make neuroscience a data-centric discipline and are key for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. This study track provides an introduction to standards and best practices that support the FAIR Principles.

These courses give introductions and overviews of some of the major statistics software packages currently used in neuroscience research.

The CAJAL Course in Computational Neuroscience teaches the central ideas, methods, and practice of modern computational neuroscience through a combination of lectures and hands-on project work. This course is designed for graduate students and postdoctoral fellows from a variety of disciplines, including neuroscience, physics, electrical engineering, computer science, mathematics, and psychology. 

The importance of Research Data Management in the conduct of open and reproducible science is better understood and technically supported than ever, and many of the underlying principles apply as much to everyday activities of a single researcher as to large-scale, multi-center open data sharing.

The human mind is a complex system that produces, processes, and transmits information in an incomparable manner. Human thoughts and actions depend profoundly on the proper function of neurons. If this function is disrupted, degeneration and disease can be the consequence. This course provides insights into state-of-the-art views on neurodegenerative, neuropsychiatric, and neuroimmunological disorders as well as clinical neuroanatomy and clinical aspects of brain imaging.

In this short series of lectures, participants will take a look at articles using TVB in a clinical context. Specifically, participants will see how TVB can help to predict recovery after stroke and how individual epileptic seizures are simulated. The course lecturers will briefly describe the methods used and results achieved in the articles.

This module covers the concepts of model predictive control, emulation of the kinematics from observations, training a policy, and predictive policy learning under uncertainty. It is a part of the Deep Learning Course at NYU's Center for Data Science, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with appli

This module covers the concepts of gradient descent, stochastic gradient descent, and momentum. It is a part of the Deep Learning Course at NYU's Center for Data Science, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Pr

Presented by the Neuroscience Information Framework (NIF), this series consists of several lectures characterizing cutting-edge, open-source software platforms and computational tools for neuroscientists. This course offers detailed descriptions of various neuroinformatic resources such as cloud-computing services, web-based annotation tools, genome browsers, and platforms for designing and building biophysically detailed models of neurons and neural ensembles.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This session will introduce the IBI and the current efforts of the Data Standards and Sharing Working Group with a view to gain input from a wider neuroscience and neuroinformatics community. 

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.

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.

These lessons give an overview of the principles underpinning the objectives, policies, and practice of Open Science, including several representative policy documents that will be increasingly relevant to neuroscience research.

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

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