The state of the field regarding the diagnosis and treatment of major depressive disorder (MDD) is discussed. Current challenges and opportunities facing the research and clinical communities are outlined, including appropriate quantitative and qualitative analyses of the heterogeneity of biological, social, and psychiatric factors which may contribute to MDD.

This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

This lesson delves into the opportunities and challenges of telepsychiatry. While novel digital approaches to clinical research and care have the potential to improve and accelerate patient outcomes, researchers and care providers must consider new population factors, such as digital disparity. 

This lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.

This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.

This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.

In this lesson, you will learn about data management within the Open Data Commons (ODC) framework, and in particular, how Spinal Cord Injury (SCI) data is stored, shared, and published. You will also hear about Frictionless Data, an open-source toolkit aimed at simplifying the data experience. 

This lesson provides an introduction the International Neuroinformatics Coordinating Facility (INCF), its mission towards FAIR neuroscience, and future directions. 

This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles. 

This video gives a brief introduction to the second session of talks from INCF's Neuroinformatics Assembly 2023. 

This talk enumerates the challenges regarding data accessibility and reusability inherent in the current scientific publication system, and discusses novel approaches to these challenges, such as the EBRAINS Live Papers platform. 

This talk describes the challenges to sustained operability and success of consortia, why many of these groups flounder after just a few years, and what steps can be taken to mitigate such outcomes. 

In this lesson, you will learn about approaches to make the field of neuroscience more open and fair, particularly regarding the integration of equality, diversity, and inclusion (EDI) as guiding principles for team collaboration. 

This lesson discusses the topic of credit and contribution in open and FAIR neuroscience, looking through the respective lenses of systems, teams, and people. 

This lesson consists of a brief discussion around this sessions previous talks. 

This talk describes how to use DataLad for your data management and curation techniques  when dealing with animal datasets, which often contain several disparate types of data, including MRI, microscopy, histology, electrocorticography, and behavioral measurements. 

This talk gives a brief overview of current efforts to collect and share the Brain Reference Architecture (BRA) data involved in the construction of a whole-brain architecture that assigns functions to major brain organs. 

This brief talk discusses the idea that music, as a naturalistic stimulus, offers a window into higher cognition and various levels of neural architecture.    

This is the third and final lecture of this course on neuroinformatics infrastructure for handling sensitive data. 

In this lecture, you will learn about virtual research environments (VREs) and their technical limitations, (i.e., a computing platform and the software stack behind it) and the security measures which should be considered during implementation.