Coding Tutorials from Online: Neuroscience
fMRI Viewing, Quality Check, Manipulating
[Stanford Center for Reproducible Neuroscience](https://reproducibility.stanford.edu/) - BIDS
- Getting Started with BIDS: Tutorial Series
- [fMRI Prep Tutorial](https://reproducibility.stanford.edu/
Functional Connectivity MRI Analysis in a network in Python
- **https://carpentries-incubator.github.io/SDC-BIDS-fMRI/instructor/aio.html
- This workshop is designed to teach you the basics and work up to performing an intra-network functional connectivity analysis of the Default Mode Network in individuals with Schizophrenia and compare them to a Control population.
- Functional Connectivity Analysis Methods and Their Interpretational Pitfalls Andre M. Bastos, MIT (September 13, 2016)
Specific Python Neuroscience Packages Methods
- building a figure in wb_view https://github.com/edickie/ciftify/blob/master/docs/tutorials/wb_view-example.md
- HCP with HPC tutorial code - preprocessing https://github.com/edickie/ciftify/blob/master/docs/tutorials/ss2017-tutorial-code.md
- How to build ROI atlas from a Network Atlas https://github.com/edickie/ciftify/blob/master/docs/tutorials/break_up_network_atlas.md
- Nilearn/Nistats pipelines for task-based functional connectivity analysis https://github.com/kfinc/nilearn-task-networks
Brain and Cognitive Sciences Computational Tutorial Series
By: MIT Open CourseWare
- Cell-Type Specific Transcriptomics Sebastian Pineda, MIT (November 21, 2022)
- Continuous-Time Deconvolutional Regression: A Method for Studying Continuous Dynamics in Naturalistic Data Cory Shain, MIT (February 28, 2022)
- Recurrent Neural Networks for Cognitive Neuroscience Guangyu Robert Yang, CBMM (August 30, 2021)
- Neural Decoding of Spike Trains and Local Field Potentials with Machine Learning in Python Omar Costilla Reyes, MIT (April 2, 2019)
- Bayesian Inference in Generative Models Luke Hewitt, MIT (November 13, 2018)
- Unsupervised Discovery of Temporal Sequences in High-Dimensional Datasets Emily Mackevicius and Andrew Bahle, MIT (April 19, 2018) *
- Reinforcement Learning Sam Gershman, Harvard (June 16, 2017)
- Functional Connectivity Analysis Methods and Their Interpretational Pitfalls Andre M. Bastos, MIT (September 13, 2016)
- Tensor Methods Anima Anandkumar, UC Irvine (July 21, 2016)
- Dynamical Systems in Neuroscience Seth Egger, MIT (January 22, 2016)
- Dimensionality Reduction II Sam Norman-Haignere, MIT (January 21, 2016)
- Dimensionality Reduction I Emily Mackevicius and Greg Ciccarelli, MIT (January 19, 2016)
- Cluster Computing and OpenMind (1 & 2) Evan Remington and Satrajit Ghosh, MIT (January 12, 2016)
- Decoding Analyses to Understand Neural Content and Coding Ethan Meyers, Hampshire College and MIT (July 23, 2015)
- Learning in Deep Neural Networks Phillip Isola, MIT (June 17, 2015)
- Learning in Recurrent Neural Networks Larry Abbott, Columbia (June 10, 2015)
- Bayesian Methods: Brain and Cognitive Perspectives Mehrdad Jazayeri and Josh Tenenbaum, MIT (June 9, 2015)
Python/Programming for Neuroimaging from INCF
Selected tutorials below. More available on INCF Website
- Jupyter Notebooks by EuroPython Conference
- Programming for Neuroimaging - Python, R, MATLAB
- Notebooks - Jupyter Notebooks and others
MIT Center for Brains, Minds + Machines - Learning Hub
- Machine Learning for Neuroscience Courses
- Machine Learning
- Bayesian Methods
- Deep Learning fmriprep-tutorial-running-the-docker-image/)