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.
- Online school for comp neuro: academy.neuromatch.io/home
- Platform for open source datasets: openneuro.org
- Great learning resources: neurohackademy.org/neurohack_year/2021
- Coursera Course: coursera.org/learn/computational-neuroscience
- MIT Course: ocw.mit.edu/courses/brain-and-cognitive-sciences
- Python for MRI Analysis: support.scinet.utoronto.ca
- Neuroimaging Analysis at Scale: support.scinet.utoronto.ca/education
- Getting Data from the Human Connectome Project (HCP): cran.r-project.org/web/packages/neurohcp/vignettes/hcp.html
Very simplified explanation of how a process could work (for fMRI)
- The data set is mostly in DICOM format (this is the case with most medical image data)
- 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)
- 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.
- 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.
- 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