- Brainhack
- Open Science
- GitHub
- Documenting projects and code
- Shell programming
- Python
- R
- Containers
- Other tools for reproducible data-science
- Open-data platforms
- Neuroimaging
- Statistics
- Machine Learning and Deep Learning
- Meta analysis
- Neuroimaging video series
- Mumford brainstats
- Andrew Jahn
- Center for Brains, Minds and Machines
- Organization from human brain mapping (OHBM)
- fMRIf summer courses from the NIH
- Conference on Cognitive Computational Neuroscience (CCN)
- [International Neuroinformatics Coordinating Facility (INCF) Annual Conference](#international-neuroinformatics-coordinating-facility-annual-conference]
- Computational and Systems Neuroscience (Cosyne) Annual Conference - 2018
- University of Michigan 2018 Training Course in fMRI
- Cambridge University MRC Cognition and Brain Sciences Unit Method Seminars 2017
- Cambridge University MRC Cognition and Brain Sciences Unit Method Seminars 2018
- ISMRM Educational Materials #fMRIF NIMH fMRI Course - from 2014 to 2018
The following list is by far not exhaustive, you will be able to find more resources in the following:
- the Neuroimaging Informatics Tools and Resources Clearinghouse
- Mariam Aly's lab wiki
- Jonathan Peelle's list of resources for beignners
- Stephan Heunis has a list to many SPM and matlab material.
- https://github.com/brainhack101 also has a collections or links to courses, data...
- open neuroscience points to a lot of open things related to neuroscience.
- a list of Scientific Coding Resource put together by neuroconscience.
- Aya Ben-Yakov compiled a great list of open-science resources for the CBU in Cambridge.
- LabHacks is a list of resources for data driven neuroscientists put together by Patrick Beukema
- a list of [open computational neuroscience resources]( https://github.com/asoplata/open-computational-neuroscience-resources/blob/master/README.md) put together by Austin Soplata
- a list of Computational resources put together by Martina Vilas
- Brainhacking by Cameron Craddock within Neurohackademy 2018 (59 min)
- Craddock, R. C., Margulies, D. S., Bellec, P., Nichols, B. N., Alcauter, S., Barrios, F. A., … Xu, T. (2016). Brainhack: a collaborative workshop for the open neuroscience community. GigaScience, 5(1).
- Introduction to Brainhack by Cameron Craddock within Brainhack Proceedings 2017 (30 min)
- Introduction to Neurohackweek 2017 by Ariel Rokem within Neurohackweek 2017
- The Open Science MOOC
- Panel discussion: fostering open communities within Neurohackademy 2018 (1 hr 30 min)
- Science: open for all by Kirstie Whitaker within Neurohackademy 2018
- Surviving and thriving as an open scientist by Tal Yarkoni within Neurohackweek 2016
- MRC Cognition and Brain Sciences Unit Open Science Day 2018 Here
- A Quick Introduction to Version Control with Git and GitHub by Bilschak et al. (2016)
- Introduction to git and GitHub by Chris Gorgolewski within Brainhack Americas (45 min)
- Git and Github 1 & 2 by Bernease Herman within Neurohackweek 2016
- Git tutorial by Eshin Jolly within MIND 2018 (36 min)
- Pro Git by Scott Chacon and Ben Straub
- What is GitHub and how to use it (17 min)
- Learn Git Branching Neat interactive introduction to Git, like an online game!
- Conquering the command line
- Learn shell
- The shell introduction I wish I had by Max Antonucci
- The Unix shell
- Andrew Jahn's Unix introduction
- Bash cheatsheet
- Explain shell commands
- Check shell scripts
- Vim interactive tutorial
- A whirlwind tour of python by Jake Vanderplas
- Introduction to Python within Brainhack Americas (45 min)
- Introduction to Python by Tal Yarkoni within Neurohackademy 2018 (1 hr 16 min)
- Python programming by Valentina Staneva within Neurohackweek 2016
- Python tips and tricks by Tal Yarkoni within Neurohackweek 2016 (58 min)
- Scientific computing with Python by Luke Chang within MIND 2018 (24 min)
- An introduction to Python! is course made by Thomas Donoghue
- Cython and numba by Ariel Rokem within Neurohackweek 2016
- Data manipulation in Python/Pandas by Tal Yarkoni within Neurohackademy 2018 (1 hr 21 min)
- High-performance Python by Ariel Rokem within Neurohackweek 2016
- Modular Software Design by Jeremy Freeman within Neurohackweek 2016 (48 min)
- Python packaging by Ariel Rokem within Neurohackademy 2018 (1 hr 26 min)
- Python Data Science Handbook by Jake Vanderplas
- Science Hacking 101 by Jeremy Manning within MIND 2018
- Software testing 1 by Chris Gorgolewski within Neurohackweek
- Software testing 2 by Chris Gorgolewski within Neurohackweek
- Testing scientific code by Chris Gorgolewski within Neurohackweek 2017 (43 min)
- Porting Python 2 code to Python 3 - official
- Programming with R by Jeanette Mumford within Neurohackweek 2016
- Introduction to Neurohacking in R MOOC on coursera
- Docker for scientists 1 by Chris Gorgolewski within Neurohackweek 2016 (1 hr 13min)
- Docker for scientists 2 by Chris Gorgolewski within Neurohackweek
- Docker tutorial by Lucy Owen within MIND 2018 (20 min)
- Neurodocker allows you to easily create containers suited to your neuroimaging needs. Here is a tutorial on how to use it.
- From interactive exploration to reproducible data science: Jupyter, Binder, Travis and friends. by Fernando Perez within Neurohackademy 2018 (1 hr 25 min)
- Jupyter tutorial by Eshin Jolly within MIND 2018 (31 min)
- Allen Institute Data and Software by Nicolas Cain within Neurohackweek 2017 (53 min)
- Allen Institute Datasets by Terri Gilbert within Neurohackweek 2016 (1 hr 8min)
- Allen Institute RNAseq data by Jeremy Miller within Neurohackweek 2016 (52 min)
- AllenSDK and the Allen Brain Observatory by Nicolas Cain and Justin Kiggins within Neurohackademy 2018 (1 hr 42 min)
- Integrating Allen Institute Datasets with MRI data by Kirstie Whitaker within Neurohackweek 2016 (28 min)
Some other platforms to get data from:
- OpenNeuro
- INDI for rawdata
- neurovault for statistical maps
- BALSA
- LORIS
- XNAT
- And there are many other possibilities
If you are looking for M/EEG data there is good list of options here
If you want to share data but your colleagues argue against it:
- Tor Wager's and Martin Lindquist's 2 parts MOOC on neuroimaging (part 1 and part 2)
- website
- manual
- mailing list
- course/tutorial: Video recordings from the AFNI bootcamp, with slides, and example data.
Nipype is best viewed as a way to create and run software-agnostic preprocessing/analysis-pipeline. It becomes very powerful when you need to use different softwares in your analysis.
Diffusion neuroimaging in Python
Advanced tools for the analysis of diffusion MRI data
- website
- community forum
- user documentation
- developer documentation
- manuscript preprint. Includes example code for both C++11 and Python libraries.
- BATMAN tutorial. MRtrix3 tutorial available on OSF with an awesome title: B.A.T.M.A.N., the Basic and Advanced Tractography with MRtrix for All Neurophiles
The following list has been shamelessly taken from the excellent repo Open Software for Human Electrophysiology. Do check it out as it also includes plugins that are not listed here.
The following are general purpose platforms, with functionality including: loading data, pre-processing, visualization, standard analysis, and making figures. They are divided in sub-sections depending on the language they use.
MNE is a general purpose tool for processing, analyzing and visualizing M/EEG data.
Wonambi is a general purpose tool for processing, analyzing and visualizing EEG data, including specific tools focused on sleep scoring and analysis.
NeuroKit is a tool for neurophysiological signal processing.
FieldTrip is a general purpose tool for processing, analyzing and visualizing M/EEG and iEEG/ECoG data.
BrainStorm is a general purpose tool for processing, analyzing and visualizing focused primarily on MEG data, but includes support for EEG & ECoG data.
EEGLab is a general purpose tool for processing, analyzing and visualizing EEG data.
SPM is a general purpose toolbox for neuroimaging, that includes support for processing M/EEG data.
NutMEG is a general purpose tool for processing, analyzing and visualizing MEG data.
EEGUtils is a general purpose tool for processing, analyzing and visualizing EEG data.
EEG.jl is an EEG processing library.
CarTool is an EEG analysis toolbox.
NeuroDSP is a package for calculating a broad range of measures on neural time series, including a range of time-domain measures such as waveform shape analyses.
Note: NeuroDSP is a tool developed by the VoytekLab.
FOOOF is a package for parameterizing neural power spectra.
Note: FOOOF is a tool developed by the VoytekLab.
Spectral connectivity is a package including a group of functional connectivity and coherence related measures.
PACTools is a package for calculating phase-amplitude coupling measures in neural time series.
Tensor PAC is a tool for calculating phase-amplitude coupling measures, using tensors and parallel computing.
PyEEG includes some implementations of information theoretic and complexity related measures for neural time series.
A collection of tools for analyzing ECoG data.
RestingIAF is a tool for estimating the peak individual alpha frequency.
Phase Opposition is a collection of functions for calculating phase opposition measures.
The Amsterdam Decoding and Modeling Toolbox does encoding and decoding model analysis on M/EEG data.
HERMES is tool for estimating connectivity measures between M/EEG signals.
SEREEGA is a package for simulating synthetic data that mimic event-related EEG activity.
Unfold is a tool for deconvolving overlapping EEG signals and for non-linear modelling.
A tool for statistical analysis of already pre-processed M/EEG data, focused mainly around the 'threshold-free cluster enhancement' method.
ERA is a tool for calculating reliability estimates for ERP data.
OpemMEEG is a package for solving forward problems for EEG & MEG data.
Be sure to check the newly formed Neuroimaging quality control task force
-
MRIQC MRI quality control. A BIDS app that runs a pipeline to assess the quality of your data.
-
the PCP Quality Assessment Protocol is another BIDS app based on the protocol of [the connectome project data}(http://preprocessed-connectomes-project.org/quality-assessment-protocol/)
-
Qoala-t for QA for freesurfer segmentations also with an online shinyapp
- Advanced time-series analysis (Dynamic Mode Decomposition) by Bing Brunton within Neurohackweek 2017 (1 hr 1 min)
- Advanced time-series analysis by Bing Brunton within Neurohackweek 2016 (1 hr 8 min)
- Data visualization by Tal Yarkoni within Neurohackademy 2018
- Efficiency and Design Optimization for fMRI by Jeanette Mumford within Neurohackweek 2017 (51 min)
- Image processing by Ariel Rokem within Neurohackweek 2016
- Modeling fMRI data by Kendrick Kay within Neurohackweek 2016 (1 hr 2 min)
- NiBabel 101 by Dan Lurie within Brainhack Americas (18 min)
- Numerical computing for neuroimaging by JB Poline within Neurohackademy 2018
- R for statistical analysis of fMRI data by Tara Madhyastha within Neurohackademy 2018 (1 hr 26 min)
- A video series on dynamic causal modeling by Kevin Aquino [6 hrs]
- A blog post tutorial on active inference (to get a better grasp on what free energy is)
- Cloud Computing for Neuroimaging 1 by Amanda Tan and Ariel Rokem within Neurohackademy 2018 (3 hr 9 min)
- Cloud Computing for Neuroimaging 2 by Tara Madhyastha within Neurohackweek 2016
- Using cloud computing for neuroimaging by Cameron Craddock within Neurohackweek 2016
- ReproNim is a good site to get up to date on doing reproducible neuroimaging research.
- Advance Unix and Make by Valentina Staneva and Tara Madhyastha within Neurohackweek 2016
- CRN resources by Chris Gorgolewski within Neurohackweek
- Improving the Reproducibility of Neuroimaging Research by Russ Poldrack within Neurohackweek 2016 (1 hr 23 min)
- GNU Make for Neuroimaging Workflows by Tara Madhyastha within Neurohackweek 2016 (48 min)
- Introduction to web technologies by Anisha Keshavan within Neurohackademy 2018 (56 min)
- Neuroimaging pipelines by Satra Ghosh within Neurohackweek 2017 (1 hr 33 min)
- Reproducibility in fMRI: What is the problem? 1 by Russ Poldrack within Neurohackweek 2017 (1 hr 40 min)
- Reproducibility in fMRI: What is the problem? 2 by Russ Poldrack within Neurohackweek 2017
- Same Data - Different Software - Different Results? Analytic Variability of Group fMRI Results by Alexander Bowring (12 mins)
- Reproducible research pipelines by Chris Gorgolewski and Satra Ghosh within Neurohackweek 2016
- Software pipelines for reproducible neuroimaging by Satra Ghosh and Chris Gorgolewski within Neurohackademy 2016 (1 hr 14 min)
- Neuroimaging Workflows & Statistics for reproducibility by Dorota Jarecka, Satrajit Ghosh, Celia Greenwood and Jean-Baptiste Poline at OHBM (3 hr 45 min)
- Tools from the Center for Open Neuroscience by Yaroslav Halchenko within MIND 2018
- Code-ocean is web based service that uses docker containers to let you run your analysis online. There is post by Stephan Heunis describing how he did that with an SPM pipeline.
- The Brain Imaging Data Structure (BIDS) presented by Chris Gorgolewski within Neurohackademy 2018 (56 min)
- the BIDS website
- the BIDS apps
- the BIDS starter kit
The Dmipy software project is dedicated to fasciliting high-level, reproducible diffusion microstructure research.
- The Dmipy open-source repository with many examples on implementing and fitting microstructure models.
- Our Preliminary Reference Paper
- Computer Vision by Michael Beyeler within Neurohackademy 2018 (53 min)
- Finding low-dimensional structure in large-scale neural recordings by Eva Dyer within Neurohackademy 2018 (1 hr 36 min)
- Interactive Data Visualization with D3 by Anisha Keshavan within Neurohackweek 2017 (49 min)
- Neuroethics by Eran Klein within Neurohackademy 2018 (58 min)
- Quantitative and diffusion MRI modeling of developmental data by Yason Yeatman within Neurohackweek
- P-values and reproducibility issues by JB Poline within Neurohackademy 2018 (1 hr 1 min)
- The evil p value by JB Poline within Neurohackweek 2017 (1 hr 2 min)
- Statistical Decision Theory by Joshua Vogelstein within Brainhack-Vienna (starts at 8 min, ends at 48 min)
- A reminder on how random field theory is used to correct for multiple comparison here.
- A primer on permutation testing (not only) for MVPA by Carsten Allefeld at OHBM 2018 (36 min)
- Cross-validation : what, which and how? by Pradeep Reedy Raamana at OHBM 2018 (30 min)
- Daniel Lakens MOOC on coursera on how to improve your statistical inferences
- Statistical thinking for the 21st century by Russ Poldrack: "I am trained as a psychologist and neuroscientist, not a statistician. However, my research on brain imaging for the last 20 years has required the use of sophisticated statistical and computational tools, and this has required me to teach myself many of the fundamental concepts of statistics. Thus, I think that I have a solid feel for what kinds of statistical methods are important in the scientific trenches."
A list of R based web based apps from shiny apps and R psychologist to help better understand:
- p-values
- confidence intervals
- p curves and why with a decent power and a large effect size, it is relatively unlikely to find a value between p<.01 and p<.05
- null hypothesis significance testing
- p hacking
- positive predictive values
- Deep learning with Keras part 1 by Ariel Rokem (2hr 3 min)
- Deep learning with Keras part 2 by Ariel Rokem
- Machine learning with scikit-learn 1 by Jake Vanderplas within Neurohackweek
- Machine learning with scikit-learn 2 by Jake Vanderplas within Neurohackweek 2017 (1 hr 20 min)
- Machine learning with scikit-learn 3 by Jake Vanderplas within Neurohackademy (2 hr 22 min)
- Deep learning and machine learing tutorials from the Montreal Artificial Intelligence and Neuroscience conference, Montreal 2018 (2 days), many authors.
- Machine learning for neuroimaging by Chris Holdgraf within Neurohackweek 2017 (1 hr 2 min)
- Machine learning in neuroimaging by Gael Varoquaux within Neurohackademy 2018 (2 hr 42 min)
- Synthesizing fMRI using generative adversarial networks: cognitive neuroscience applications, promises and pitfalls by Sanmi Koyejo within Neurohackademy 2018 (1 hr 7 min)
- Introduction to Keras by Anisha Keshavan within OHBM DL Educational Course 2018
- Introduction to Keras & Interpretability Methods by Andrew Doyle within MAIN 2018 Hands-on DL course
- Brain Segmentation in Keras by Thomas Funck within MAIN 2018 Hands-on DL course
They are divided in sub-sections depending on the language they use.
Intended to ease statistical learning analyses of large datasets.
Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data.
neuropredict is an easy to use Python tool for comprehensive evaluation of predictive power of popular ML techniques for features-to-target prediction (such as biomarkers to disease and similar variations)
BrainIAK applies advanced machine learning methods and high-performance computing to analyzing neuroimaging data. We also have tutorials that cover topics from basics to advanced techniques.
TDT is the The Decoding Toolbox.
PRoNTo is the Pattern Recognition for Neuroimaging Toolbox developed at UCL (UK).
The pattern components modelling toolbox of the Diedrichsen lab
From Carsten Allefeld
- Overview_of_Meta-Analysis_Approaches by Tom Nichols at OHBM 2018 with slides (18 min)
- ALE and BrainMap by Simon B. Eickhoff at OHBM 2018 (22 min)
NiMARE is a Python library for coordinate- and image-based meta-analysis. Chris Gorgolewski wrote a tutorial on how to use it.
For coordinate based meta-analysis:
For image based meta-analysis:
- IBMA is the Image-Based Meta-Analysis toolbox for SPM.
Either on youtube or on some other platform
Lecture series on neuroimaging and electrophysiology from the Neurohackademy summer school.
Mike Cohen's lecturelets on time series data analysis here.
Jeanette Mumford series of videos on neuroimaging analysis on youtube. The channel also has Facebook group.
Here for the videos with 'tutorials' for FSL, SPM, Freesurfer and AFNI amongst other things.
The videos of the lectures and workshops from the previous HBM conferences are available online here.
This conference has the videos from its first edition here