This lesson is in the early stages of development (Alpha version)

Functional Neuroimaging Analysis in Python

FIXME: Add information about fMRI analysis in Neuroimaging

Prerequisites

Attendees must have some base familiarity with Python in order to comfortably progress through the lesson

Schedule

Setup Download files required for the lesson
00:00 1. Exploring Preprocessed fMRI Data from fMRIPREP How does fMRIPrep store preprocessed neuroimaging data
How do I access preprocessed neuroimaging data
00:25 2. Introduction to Image Manipulation using Nilearn How can be perform arithmetic operations on MR images
01:10 3. Integrating Functional Data How is fMRI data represented
How can we access fMRI data along spatial and temporal dimensions
How can we integrate fMRI and structural MRI together
01:55 4. Preprocessing fMRI Data What are the standard preprocessing steps?
What existing pipelines help with preprocessing?
02:25 5. Cleaning Confounders in your Data with Nilearn How can we clean the data so that it more closely reflects BOLD instead of artifacts
03:05 6. Applying Parcellations to Resting State Data How can we reduce amount of noise-related variance in our data?
How can we frame our data as a set of meaningful features?
03:50 7. Functional Connectivity Analysis How can we estimate brain functional connectivity patterns from resting state data?
04:35 8. Neuroimaging Fundamentals & Nibabel How are images loaded in Python?
05:05 9. Introduction to Image Manipulation using Nilearn How can be perform arithmetic operations on MR images
05:50 10. Exploration of Open Neuroimaging Datasets in BIDS format How does standardization of neuroimaging data ease the data exploration process
06:35 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.