This lesson discusses FAIR principles and methods currently in development for assessing FAIRness.
This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.
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 explains the fundamental principles of neuronal communication, such as neuronal spiking, membrane potentials, and cellular excitability, and how these electrophysiological features of the brain may be modelled and 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 lecture covers the emergence of cognitive science after the Second World War as an interdisciplinary field for studying the mind, with influences from anthropology, cybernetics, and artificial intelligence.
This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein.
This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved.
In this lesson you will learn about the motivation behind manipulating neural activity, and what forms that may take in various experimental designs.
This lesson provides an introduction to neurons, synaptic transmission, and ion channels.
This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.
This lecture covers integrating information within a network, modulating and controlling networks, functions and dysfunctions of hippocampal networks, and the integrative network controlling sleep and arousal.
This lecture focuses on the comprehension of nociception and pain sensation, highlighting how the somatosensory system and different molecular partners are involved in nociception.
This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models.
This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted.
While the previous lesson in the Neuro4ML course dealt with the mechanisms involved in individual synapses, this lesson discusses how synapses and their neurons' firing patterns may change over time.
Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks.
This lecture provides an overview of some of the essential concepts in neuropharmacology (e.g. receptor binding, agonism, antagonism), an introduction to pharmacodynamics and pharmacokinetics, and an overview of the drug discovery process relative to diseases of the central nervous system.
This lesson describes the fundamentals of genomics, from central dogma to design and implementation of GWAS, to the computation, analysis, and interpretation of polygenic risk scores.
This lesson contains the slides (pptx) of a lecture discussing the necessary concepts and tools for taking into account population stratification and admixture in the context of genome-wide association studies (GWAS). The free-access software Tractor and its advantages in GWAS are also discussed.