This lecture discusses the the importance and need for data sharing in clinical neuroscience.
This lecture presents the Medical Informatic Platform's data federation for Traumatic Brain Injury.
This lecture gives insights into the Medical Informatics Platform's current and future data privacy model.
This lecture explains the concept of federated analysis in the context of medical data, associated challenges. The lecture also presents an example of hospital federations via the Medical Informatics Platform.
This lecture gives an overview on the European Health Dataspace.
This lecture presents the Medical Informatics Platform's data federation in epilepsy.
This lecture presents the Medical Informatics Platform's data federation in epilepsy.
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 lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.
This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology.
This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs.
In this final lecture of the INCF Short Course: Introduction to Neuroinformatics, you will hear about new advances in the application of machine learning methods to clinical neuroscience data. In particular, this talk discusses the performance of SynthSeg, an image segmentation tool for automated analysis of highly heterogeneous brain MRI clinical scans.
This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.
In this lesson, you will learn about the connectome, the collective system of neural pathways in an organism, with a closer look at the neurons, synapses, and connections of particular species.
This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein.
This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.
In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?
This tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.
The tutorial on modelling strokes in TVB includes a didactic video and jupyter notebooks (reproducing publication: Falcon et al. 2016 eNeuro).