This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.
This lecture covers infrared LED oblique illumination for studying neuronal circuits in in vitro block-preparations of the spinal cord and brain stem.
This lecture covers the application of diffusion MRI for clinical and preclinical studies.
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 walks participants through the application of dynamic causal modelling (DCM) to fMRI data using MATLAB. Participants are also shown various forms of DCM, how to generate and specify different models, and how to fit them to simulated neural and BOLD data.
This lesson corresponds to slides 158-187 of the PDF below.
This brief talk goes into work being done at The Alan Turing Institute to solve real-world challenges and democratize computer vision methods to support interdisciplinary and international researchers.
This lightning talk describes the heterogeneity of the MR field regarding types of scanners, data formats, protocols, and software/hardware versions, as well as the challenges and opportunities for unifying these datasets in a common interface, MRdataset.
This brief talk covers an analysis technique for multi-band, multi-echo fMRI data, applying a denoising framework which can be used in an automated pipeline.
In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks.
Video coming soon.
This talk covers the differences between applying HED annotation to fMRI datasets versus other neuroimaging practices, and also introduces an analysis pipeline using HED tags.
In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI).
This lesson provides a thorough description of neuroimaging development over time, both conceptually and technologically. You will learn about the fundamentals of imaging techniques such as MRI and PET, as well as how the resultant data may be used to generate novel data visualization schemas.
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.
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.