## Basic Toolboxes for MRI analysis - [FMRIB Software Library(FSL)][1]: An integrative library of analysis tools for FMRI, MRI and DTI brain imaging data. - [Installment][2] - [Statistical Parametric Mapping 12(SPM12)][3]: MATLAB package for analyzing MRI data - [Installment][4] - Other extensions - [GLMsingle][5] [[Prince et al., 2022]][6]: is a toolbox for obtaining accurate single-trial estimates in fMRI time-series data. We provide both MATLAB and Python implementations. [Movie][7] - [MarsBaR: Region of interest toolbox for SPM][8] - [pTFCE: Probabilistic Threshold-free Cluster Enhancement][9]: A cluster-enahncement method to improve detectability of neuroimaging signal by the Predictive Neuroimagiong Laboratory ([PNI-lab][10]) of the University Hospital Essen, Germany. - [Statistical Non-Parameteric Mapping(SnPM13)][11]: an extended SPM toolbox for non-parametric permutation/randomisation tests using the General Linear Model and pseudo t-statistics for independent observations. - - [nipy][12]: Python package for analyzing MRI data analysis - [hMRI][13] [[Tabelow et al., 2019]][14]: A MATLAB toolbox for quantitative MRI and in vivo histology using MRI. - [Quantitative Susceptibility Mapping (QSM):][15] :Quantitative Susceptibility Mapping (QSM) provides a map of local tissue magnetic susceptibility. QSM is a novel contrast mechanism in MRI compared to traditional hypointensity contrast in SWI or T2* weighted images that only allow detection of the presence of tissue susceptibility. - [MRtrix3][16]: Advanced diffusion MRI tools for the analysis. - [Documents][17] ## Preprocessing Toolbox - [mriqc][18]: a tool for automated prediction of quality control. - [fibr: quality control of diffusion MRI images][19]: quality control of diffusion MRI images from [the Healthy Brain Network][20]. - [fMRIPrep: A Robust Preprocessing Pipeline for fMRI Data][21]: developed by the Poldrack lab at Stanford University for use at the Center for Reproducible Neuroscience (CRN), as well as for open-source software distribution. - [Connectome Spatial Smoothing (CSS)][22][ [Mansour et al., 2022]][23]: computationally efficient methods to perform CSS. ## Visualization Toolbox - [MRIcron][24]: 2D nifti data visualization - [MRICronGL][25]: 3D nifti data visualization - [ITK-SNAP][26]: A software application used to segment structures in 3D medical images. - [AFQ-Browser][27]: This software generates a browser-based visualization of data processed with the Automated Fiber Quantification (AFQ) software for diffusion tensor imaging. - Brain Statistics with ggseg and ggseg3d [\[Mowinchkel and Vidal-Pineiro, 2020\]][28]: [R-packages][29] and [Python packages][30] ## Network visualization Toolbox - [brainGraph][31]: R package for ROI network visualization - [brainconn][32]: a R-package for plotting brain connectivity data - [BrainNet Viewer][33][[Xia et al., 2013]][34]: package for ROI network visualization - ## Static/Dynamic Network Analysis - [Brain Connectivity Toolbox][35] [[Rubinov and Sporns, NeuroImage, 2010]][36]: Brain network analysis MATLAB toolbox - [BrainSpace][37] [[Vos de Wael et al., Commun. Biol., 2020]][38]: BrainSpace is a lightweight cross-platform toolbox primarily intended for macroscale gradient mapping and analysis of neuroimaging and connectome level data. - [A toolbox for co-activation pattern analysis][39] [[Bolton et al., NeuroImage, 2020]][40] ## Functional MRI analysis ### analysis platform - [BRANT: A Versatile and Extendable Resting-state fMRI Toolkit][41] [[Xu et al., Front. Neuroinform., 2018]][42] - [functional connectivity toolbox (CONN)][43] - [QuNex] [[Ji et al., 2023]][44]: An integrative platform for reproducible neuroimaging analytics ### [Center for Translational Research in Neuroimaging & Data Science (TReNDs) Toolboxes][45] - [Group ICA Toolbox (GIFT)][46] ## Neuromap Comparisons - [neuromaps][47] [[Markello et al., 2022]][48]: A toolbox is designed to help researchers make easy, statistically-rigorous comparisons between brain maps (or brain annotations). [tweets][49] [[Voytek, 2022]][50] ### Advanced MRI analysis - [Brain Activity Flow ("Actflow") Toolbox][51] [[Cocuzza et al., 2022]][52] [Tweet][53] - [PyMVPA][54] [[Hanke et al., Neuroinformatics, 2009]][55] - [Edge-centric time series analysis][56] [[Novelli and Razi, 2022, Nat Commun]][57] - [nctpy][58]: Network Control Theory for Python - [nilearn][59]: A Python module for fast and easy statistical learning on NeuroImaging data. - [neuropredict][60] [[Raamana Strother et al., JOSS, 2017]][61] - [BrainAK][62]:A Python package for advanced machine learning methods and high-performance computing to analyzing neuroimaging data - [Brainstats][63]: a MATLAB and Python toolbox for the statistical analysis and context decoding of neuroimaging data. [document][64] [[Vos de Wael et al., 2022]][65] - [The Decoding Toolbox (TDT)][66] [[Hebart et al., Front. Neutoinform., 2015]][67] - [ProNTo: Pattern Recognition for Neuroimaging Toolbox][68] [[Schrouff et al., Neuroinformatics, 2013]][69] - [Representational Similarity Analysis(RSA)][70] [[Nili et al., Plos Comp. Biol., 2014]][71] - [Pattern component modeling(PCM) toolbox][72] [[Diedrichsen et al., NeuroImage, 2018]][73] - [cvMANOVA][74] [[Allefeld et al., NeuroImage, 2014]][75] - [CBP tools][76] [[Reuter et al., 2020]][77]: A Python package for regional connectivity-based parcellatio - [highly comparative time-series analysis][78] a Matlab software package for running highly comparative time-series analysis. It extracts thousands of time-series features from a collection of univariate time series and includes a range of tools for visualizing and analyzing the resulting time-series feature matrix. - [hcga][79] [[Peach et al., 2021]][80]: a highly comparative graph analysis toolbox. It performs a massive feature extraction from a set of graphs, and applies supervised classification methods. - [Translational Algorithms for Psychiatry-Advancing Science (TAPAS)][81] [[Frassle et al., NeuroImage, 2017]][82]: collection of algorithms and software tools developed by the Translational Neuromodeling Unit (TNU, Zurich) and collaborators including regression Dynamic Causal Modeling (rDCM) and Continuous Extension of ODE methods (ceode) to model effective connectivity and evoked responses. - [Neuroscout][83]: an online platform for fast and flexible analysis of fMRI data. - [DeepMReye][84] [[Frey et al., 2021]][85]: magnetic resonance-based eye tracking using deep neural networks and explanations are available from [twitter][86]. - [Python Toolkit of Statistics for Pairwise Interactions (pyspi)][87] [[Cliff et al., 2022]][88] a comprehensive python library for computing statistics of pairwise interactions (SPIs) from multivariate time-series (MTS) data. - [resting state HRF (rsHRF)][89] [[Wu et al., 2021]][90]: A Toolbox for Resting State HRF Deconvolution and Connectivity Analysis [enter link description here][91] - [Python-based Reliability in MRI (PyReliMRI)][92]: An open-source python package that will provide multiple reliability metrics, at the group and individual level, that researchers may use to report in their manuscripts in cases of multi-run and/or multi-session data. - [pyDecNef][93]: [[Margolles et al., 2023]][94] An open and straightforward framework to perform real-time fMRI decoded neurofeedback studies in Python. [1]: https://brainiak.org/tutorials/ [2]: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation [3]: https://brainiak.org/tutorials/ [4]: https://www.fil.ion.ucl.ac.uk/spm/software/download/ [5]: https://github.com/cvnlab/GLMsingle#readme [6]: https://www.biorxiv.org/content/10.1101/2022.01.31.478431v1 [7]: https://www.youtube.com/watch?v=yb3Nn7Han8o [8]: http://marsbar.sourceforge.net/ [9]: https://spisakt.github.io/pTFCE/ [10]: https://pni-lab.github.io/ [11]: https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/software/snpm [12]: https://nipy.org/index.html [13]: https://hmri-group.github.io/hMRI-toolbox/ [14]: https://www.sciencedirect.com/science/article/pii/S1053811919300291 [15]: http://pre.weill.cornell.edu/mri/pages/qsm.html#:~:text=MEDI%20Toolbox%20is%20a%20collection,Laplacian%20Boundary%20Value%20%28LBV%29. [16]: https://www.mrtrix.org/ [17]: https://mrtrix.readthedocs.io/en/latest/# [18]: https://mriqc.readthedocs.io/en/stable/index.html [19]: https://fibr.dev/#/about [20]: https://healthybrainnetwork.org/ [21]: https://fmriprep.readthedocs.io/en/stable/# [22]: https://github.com/sina-mansour/connectome-spatial-smoothing [23]: https://www.sciencedirect.com/science/article/pii/S1053811922000593 [24]: https://people.cas.sc.edu/rorden/mricron/index.html [25]: https://www.nitrc.org/projects/mricrogl/ [26]: http://www.itksnap.org/pmwiki/pmwiki.php?n=Main.HomePage [27]: https://yeatmanlab.github.io/AFQ-Browser/ [28]: https://journals.sagepub.com/doi/10.1177/2515245920928009 [29]: https://github.com/Athanasiamo/ggsegpaper [30]: https://github.com/ggseg/python-ggseg [31]: https://github.com/cwatson/brainGraph [32]: https://sidchop.github.io/brainconn/articles/brainconn.html [33]: https://www.nitrc.org/projects/bnv/ [34]: https://doi.org/10.1371/journal.pone.0068910 [35]: https://sites.google.com/site/bctnet/ [36]: https://doi.org/10.1016/j.neuroimage.2009.10.003 [37]: https://brainspace.readthedocs.io/en/latest/ [38]: https://doi.org/10.1038/s42003-020-0794-7 [39]: https://c4science.ch/source/CAP_Toolbox/ [40]: https://doi.org/10.1016/j.neuroimage.2020.116621 [41]: https://sphinx-doc-brant.readthedocs.io/en/latest/index.html [42]: https://doi.org/10.3389/fninf.2018.00052 [43]: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007549 [44]: https://www.frontiersin.org/articles/10.3389/fninf.2023.1104508/full [45]: https://trendscenter.org/software/ [46]: https://trendscenter.org/software/ [47]: https://github.com/netneurolab/neuromaps [48]: https://doi.org/10.1038/s41592-022-01625-w [49]: https://twitter.com/misicbata/status/1578048608141819905 [50]: https://doi.org/10.1038/s41592-022-01630-z [51]: https://colelab.github.io/ActflowToolbox/ [52]: https://www.sciencedirect.com/science/article/pii/S2666166721008005?via=ihub [53]: https://twitter.com/carrisa_cocuzza/status/1491063622386458626 [54]: https://github.com/PyMVPA/PyMVPA [55]: https://doi.org/10.1007/s12021-008-9041-y [56]: https://github.com/LNov/eFC [57]: https://www.nature.com/articles/s41467-022-29775-7#citeas [58]: https://nctpy.readthedocs.io/en/latest/index.html [59]: https://nilearn.github.io/# [60]: https://github.com/raamana/neuropredict [61]: http://%20http://doi.org/10.21105/joss.00382 [62]: https://brainiak.org/#analyses [63]: https://github.com/MICA-MNI/BrainStat [64]: https://brainstat.readthedocs.io/en/master/ [65]: https://www.biorxiv.org/content/10.1101/2022.01.18.476795v3.abstract??collection= [66]: https://sites.google.com/site/tdtdecodingtoolbox/ [67]: https://doi.org/10.3389/fninf.2014.00088 [68]: http://www.mlnl.cs.ucl.ac.uk/pronto/index.html [69]: https://doi.org/10.1007/s12021-013-9178-1 [70]: https://github.com/rsagroup/rsatoolbox [71]: https://doi.org/10.1371/journal.pcbi.1003553 [72]: https://github.com/jdiedrichsen/pcm_toolbox [73]: https://doi.org/10.1016/j.neuroimage.2017.08.051 [74]: https://github.com/allefeld/cvmanova [75]: https://www.sciencedirect.com/science/article/abs/pii/S1053811913011920?via=ihub [76]: https://pypi.org/project/cbptools/ [77]: https://www.ncbi.nlm.nih.gov/pubmed/32144496 [78]: https://github.com/benfulcher/hctsa [79]: https://github.com/barahona-research-group/hcga [80]: https://doi.org/10.1016/j.patter.2021.100227 [81]: https://translationalneuromodeling.github.io/tapas/ [82]: https://pubmed.ncbi.nlm.nih.gov/28259780/ [83]: https://neuroscout.github.io/neuroscout/ [84]: https://github.com/DeepMReye/DeepMReye [85]: https://doi.org/10.1038/s41593-021-00947-w [86]: https://twitter.com/NauMatt/status/1457742155859038217 [87]: https://github.com/olivercliff/pyspi [88]: https://arxiv.org/abs/2201.11941 [89]: https://github.com/bids-apps/rsHRF/tree/1.5.8 [90]: https://www.sciencedirect.com/science/article/pii/S1053811921008648 [91]: https://github.com/compneuro-da/rsHRF [92]: https://github.com/demidenm/PyReliMRI/ [93]: https://pedromargolles.github.io/pyDecNef/ [94]: https://www.biorxiv.org/content/10.1101/2023.07.04.547632v1