List of neuroimaging software

Short description: none

Neuroimaging software is used to study the structure and function of the brain. To see an NIH Blueprint for Neuroscience Research funded clearinghouse of many of these software applications, as well as hardware, etc. go to the NITRC web site.

* 3D Slicer Extensible, free open source multi-purpose software for visualization and analysis.
* Amira 3D visualization and analysis software
* Analysis of Functional NeuroImages (AFNI)
* Analyze developed by the Biomedical Imaging Resource (BIR) at Mayo Clinic.
* Brain Image Analysis Package
* CamBA
* Caret Van Essen Lab, Washington University in St. Louis
* Computational anatomy toolbox (CAT)
* CONN (functional connectivity toolbox)
* Diffusion Imaging in Python (DIPY)[1]
* DL+DiReCT[2]
* EEGLAB
* FMRIB Software Library (FSL)
* FreeSurfer
* Imaris Imaris for Neuroscientists
* ISAS (Ictal-Interictal SPECT Analysis by SPM)
* LONI Pipeline, Laboratory of Neuro Imaging, USC
* Lead-DBS
* Mango[3]
* NITRC The Neuroimaging Informatics Tools and Resources Clearinghouse. An NIH funded database of neuroimaging tools
* NeuroKit, a Python open source toolbox for physiological signal processing
* Neurophysiological Biomarker Toolbox
* PyNets: A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning (PyNets)
* Seed-based d mapping (previously signed differential mapping, SDM): a method for conducting meta-analyses of voxel-based neuroimaging studies.
* The Spinal Cord Toolbox (SCT) is the first comprehensive and open-source software for processing MR images of the spinal cord.[4]
* Statistical parametric mapping (SPM)

## References

1. ↑ Garyfallidis, Eleftherios; Brett, Matthew; Amirbekian, Bagrat; Rokem, Ariel; van der Walt, Stefan; Descoteaux, Maxime; Nimmo-Smith, Ian; Dipy, Contributors (2014). "Dipy, a library for the analysis of diffusion MRI data". Frontiers in Neuroinformatics 8: 8. doi:10.3389/fninf.2014.00008. ISSN 1662-5196. PMID 24600385.
2. ↑ Rebsamen, Michael; Rummel, Christian; Reyes, Mauricio; Wiest, Roland; McKinley, Richard (December 2020). "Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation". Human Brain Mapping 41 (17): 4804–4814. doi:10.1002/hbm.25159. PMID 32786059. PMC 7643371. https://doi.org/10.1002/hbm.25159.
3. ↑ "Different patterns of brain activation in normal aging and Alzheimer's disease from cognitional sight: meta analysis using activation likelihood estimation". Journal of the Neurological Sciences 343 (1): 159–66. 2014. doi:10.1016/j.jns.2014.05.066. PMID 24950901.
4. ↑ Cohen-Adad J, De Leener B, Benhamou M, Cadotte D, Fleet D, Cadotte A, Fehlings MG, Pelletier Paquette JP, Thong W, Taso M, Collins DL, Callot V, Fonov V. Spinal Cord Toolbox: an open-source framework for processing spinal cord MRI data. Proceedings of the 20th Annual Meeting of OHBM, Hamburg, Germany 2014:3633

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