This lesson consists of lecture and tutorial components, focusing on resources and tools which facilitate multi-scale brain modeling and simulation.
In this talk, challenges of handling complex neuroscientific data are discussed, as well as tools and services for the annotation, organization, storage, and sharing of these data.
This lecture describes the neuroscience data respository G-Node Infrastructure (GIN), which provides platform independent data access and enables easy data publishing.
This lesson provides an introduction to the course Neuroscience Data Integration Through Use of Digital Brain Atlases, during which attendees will learn about concepts for integration of research data, approaches and resources for assigning anatomical location to brain data, and infrastructure for sharing experimental brain research data.
This talk covers the various concepts, motivations, and trends within the neuroscientific community related to the sharing and integration of brain research data.
This lesson focuses on the neuroanatomy of the human brain, delving into macrostructures like cortices, lobes, and hemispheres, and microstructures like neurons and cortical laminae.
This lesson provides an introduction to the European open research infrastructure EBRAINS and its digital brain atlas resources.
In this lesson, attendees will learn about the challenges in assigning experimental brain data to specific locations, as well as the advantages and shortcomings of current location assignment procedures.
This lesson covers the inherent difficulties associated with integrating neuroscientific data, as well as the current methods and approaches to do so.
Attendees of this talk will learn about QuickNII, a tool for user-guided affine registration of 2D experimental image data to 3D atlas reference spaces, which also facilitates data integration through standardized coordinate systems.
This lesson provides an overview of DeepSlice, a Python package which aligns histology to the Allen Brain Atlas and Waxholm Rat Atlas using deep learning.
This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.
This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity.
This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.
This is the second of three lectures around current challenges and opportunities facing neuroinformatic infrastructure for handling sensitive data.
In this lesson you will learn about current efforts towards integrating multimodal human brain data using the open source SCORE HED library schema.
This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient.
This lecture will highlight our current understanding and recent developments in the field of neurodegenerative disease research, as well as the future of diagnostics and treatment of neurodegenerative diseases.
This lecture continues from part one (previous lesson), highlighting our current understanding and recent developments in the field of neurodegenerative disease research, as well as the future of diagnostics and treatment of neurodegenerative diseases.
This lecture picks up from the previous lesson, providing an overview of neuroimaging techniques and their clinical applications.