Tools & Resources for Computational Neuroscience beginners

Recently someone asked me if I could send them some good resources for getting started in comp neuroscience. So I’ve collected a few things from the last few years and will put them together here in one place.


Learning Resources


Very simplified explanation of how a process could work (for fMRI)

  1. The data set is mostly in DICOM format (this is the case with most medical image data)
  2. First you have to convert the DICOM data to the NIfTI format -> For this you need the DICOM to NIfTI converter (people.cas.sc.edu/rorden/mricron/dcm2nii.html)
  3. If you then have the NIfTI data you have to bring it into the so called BIDS format, because all people have agreed to bring brain data always into this structure.
  4. For preprocessing there is fortunately already a ready pipeline that you can use (fmriprep.org). Important here is that you can use the pipeline only if your data is in the BIDS format.
  5. After that you can train your algorithm with Nilearn (nilearn.github.io/) and/or Nibabel (nipy.org/nibabel/).

Python tools for computational neuroscience

Nipy – Community that creates many Python tools
Nipype – Pipeline for Neuroimaging data
Pydra – Dataflow engine for Nipype (Nipype 2.0)
Nilearn – Provides ML tools for Analysis (Included in Nipype)(Makes it easy to use sci-kit learn)
NiBabel – Accessing a cacophony of neuroimaging file formats
fMRIPrep – Preprocessing Pipeline for fMRI Data (Build on FreeSurfer)
dcm2nii – DICOM to NIfTI conversion (Included in Nipype)
Nipreps – NeuroImaging PREProcessing tools
PyBIDS – Python library to centralize interactions with datasets conforming BIDS format
TemplateFlow – Version-controlled resource that allows researchers to use templates “off-the-shelf” and share new ones (Part of Nipreps )
NiWorkflows – Robust processing tools for MRI data (Part of Nipreps )


Note: Let me know if I got something wrong or If I should add something

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