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 tutorial demonstrates how to perform cell-type deconvolution in order to estimate how proportions of cell-types in the brain change in response to various conditions. While these techniques may be useful in addressing a wide range of scientific questions, this tutorial will focus on the cellular changes associated with major depression (MDD).
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 discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.
This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.
This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling.
In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?
This lesson gives an introductory presentation on how data science can help with scientific reproducibility.
This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research.
This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.
This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.