Coding Tutorials from Online: Neuroscience

Date: February 26, 2024 4:10 PM

Neuroscience Coding Tutorials

Structure and background:

• The Good Research Code Handbook

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

  ciftify usage example https://github.com/edickie/ciftify/blob/master/docs/tutorials/example-usage.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

• 2022–2023 • FindingFive: An Online, Non-Profit Platform for Behavioral Research Ting Qian and Noah Nelson, FindingFive (April 28, 2023) • Diffusion and Score-Based Generative Models Yang Song, Stanford University (December 12, 2022) • Cell-Type Specific Transcriptomics Sebastian Pineda, MIT (November 21, 2022) • Tutorial on Statistical Inference on Representational Geometries Heiko Schütt, NYU (October 25, 2022) • GLMsingle: A Toolbox for Improving Single-Trial fMRI Response Estimates Jacob Prince, MIT (April 28, 2022) • ThreeDWorld (TDW) Tutorial Jeremy Schwartz and Seth Alter, MIT (April 1, 2022) • Continuous-Time Deconvolutional Regression: A Method for Studying Continuous Dynamics in Naturalistic Data Cory Shain, MIT (February 28, 2022)
• 2020–2021 • Recurrent Neural Networks for Cognitive Neuroscience Guangyu Robert Yang, CBMM (August 30, 2021) • Suite2P: A Fast and Accurate Pipeline for Automatically Processing Functional Imaging Recordings Carsen Stringer, HHMI Janelia Research Campus (July 29, 2021) • Learning What We Know and Knowing What We Learn: Gaussian Process Priors for Neural Data Analysis Guillaume Hennequin and Kris Jensen, University of Cambridge (July 8, 2021) • Exiting Flatland: Measuring, Modeling, and Synthesizing Animal Behavior in 3D Jesse Marshall, Harvard University (April 8, 2021) • Linear Analysis of RNN Dynamics Eli Pollock, MIT (November 19, 2020) • Nonlinear Dimensionality Reduction Christian Bueno, University of California, Santa Barbara (September 22, 2020) • Using Lookit to Run Developmental Studies Online Maddie Pelz, MIT (September 3, 2020) • Adversarial Examples and Human-ML Alignment Shibani Santurkar, MIT (July 23, 2020) • Decoding Animal Behavior Through Pose Tracking Talmo Pereira, Princeton University (July 9, 2020)
• 2017–2019 • Principles and Applications of Relational Inductive Biases in Deep Learning Kelsey Allen, MIT (April 11, 2019) • 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) • Dimensionality Reduction for Matrix- and Tensor-Coded Data (1 & 2) Alex Williams, Stanford (September 5, 2017) • Calcium Imaging Data Cell Extraction Pengcheng Zhou, Columbia (July 12, 2017) • Reinforcement Learning Sam Gershman, Harvard (June 16, 2017) • Better Science Code Eric Denovellis, BU (May 10, 2017)  • An Introduction to LSTMs in TensorFlow Harini Suresh and Nick Locascio, MIT (April 26, 2017)  • An Introduction to Spike Sorting Jai Bhagat and Caroline Moore-Kochlacs, MIT (March 22, 2017)
• 2015–2016 • Linear Network Theory and Sloppy Models Mark Goldman, UC Davis (November 21, 2016)            • 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)

INCF Python/Programming for Neuroimaging

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

[Stanford Center for Reproducible Neuroscience](https://reproducibility.stanford.edu/) - BIDS

Getting Started with BIDS: Tutorial Series

fMRI Prep Tutorial

MeTrics Lab

Various computational neuroscience Tutorials

Machine Learning for Biomedical Applications (MLBA) course

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