List of functional connectivity software

Functional connectivity software is used to study functional properties of the connectome using functional Magnetic Resonance Imaging (fMRI) data in the resting state and during tasks. To access many of these software applications visit the NIH funded Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) site.

Name Description Programming language Is part of / requires Developer/Organization
Brain Connectivity Toolbox[1] Graph-theoretical analyses of functional connectivity Matlab Department of Psychological and Brain Sciences, Indiana University
BrainNet viewer[2] Brain network visualization tool Matlab National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
C-PAC[3] Functional connectivity analysis pipeline Python Child Mind Institute; Nathan Kline Institute; NYU Langone Medical Center
CONN[4] Functional connectivity analysis and display tool Matlab SPM McGovern Institute for Brain Research, Massachusetts Institute of Technology: MIT
Connectome workbench Visualization and discovery tool Python Child Mind Institute, Nathan Kline Institute, NYU Langone Medical Center
cPPI[5] Task-related functional connectivity analysis Matlab SPM Monash Clinical and Imaging Neuroscience
DCM[6] Dynamic Causal Modelling analysis Matlab SPM Wellcome Trust Centre for Neuroimaging, University College London
FATCAT[7] Functional and tractographic connectivity analysis C AFNI Scientific and Statistical Computing Core, National Institute of Mental Health: NIMH
FSFC[8] Seed-based functional connectivity analysis Shell FreeSurfer Martinos Center for Biomedical Imaging
Fubraconnex[9] Tool for visual analysis of functional connectivity C Delft University of Technology
GIFT[10] Independent component analysis Matlab Medical Image Analysis Lab, The Mind Research Network
gPPI[11] Task-related functional connectivity analysis Matlab SPM University of Wisconsin Madison
Graphvar[12] Graph-theoretical analysis tool Matlab Division of Mind and Brain Research, Charité Universitätsmedizin
Graph Theoretic GLM Toolbox[13] Graph theory analysis and fMRI preprocessing pipeline Matlab Boston University School of Medicine, VA Boston Healthcare System
MELODIC[14] Independent component analysis C FSL Functional Magnetic Resonance Imaging of the Brain Analysis Group, Oxford University
NIAK[15] Neuroimaging analysis library Matlab, Octave Research Centre of the Montreal Geriatric Institute, University of Montreal
nilearn[16] Machine learning for Neuro-Imaging in Python Python INRIA Parietal Project Team, Neurospin, CEA Institute
REST[17] Resting-state functional connectivity analysis tool Matlab State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University

See also

References

  1. ↑ Rubinov, M. & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52,1059–1069
  2. ↑ Xia, M., Wang, J. & He, Y. (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8,e68910
  3. ↑ Di Martino, A. et al. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecturein autism. Mol. Psychiatry 19, 659–667
  4. ↑ Whitfield-Gabrieli, S. & Nieto-Castanon, A. (2012). Conn: a functional connectivity toolbox for correlated and anticorrelated brainnetworks. Brain Connect 2, 125–141
  5. ↑ Fornito, A., Harrison, B. J., Zalesky, A., and Simons, J.S. (2012). Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. PNAS 109 (31) 12788-12793
  6. ↑ Friston, K. J., Kahan, J., Biswal, B. & Razi, A. (2014). A DCM for resting state fMRI. Neuroimage 94, 396–407 .
  7. ↑ Taylor, P. A. & Saad, Z. S. (2013). FATCAT: (an efficient) Functional and Tractographic Connectivity Analysis Toolbox. Brain Connect 3,523–535
  8. ↑ Fischl, B. FreeSurfer. (2012). Neuroimage 62, 774–781
  9. ↑ van Dixhoorn, A.F., Vissers, B., Ferrarini, L., Milles, J., and Botha, C.P. (2010). Visual analysis of integrated resting state functional brain connectivity and anatomy, Eurographics Workshop on Visual Computing for Biology and Medicine
  10. ↑ Calhoun, V. D., Adali, T., Pearlson, G. D. & Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–151
  11. ↑ McLaren, D.G., Ries, M.L., Xu, G., Johnson, S.C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches, NeuroImage, 61(4), 1277-1286
  12. ↑ Kruschwitz, J. D., List, D., Waller, L., Rubinov, M., & Walter, H. (2015). GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity. Journal of neuroscience methods, 245, 107-115.
  13. ↑ Spielberg, Jeffrey M.; McGlinchey, Regina E.; Milberg, William P.; Salat, David H. "Brain Network Disturbance Related to Posttraumatic Stress and Traumatic Brain Injury in Veterans". Biological Psychiatry 78 (3): 210–216. doi:10.1016/j.biopsych.2015.02.013.
  14. ↑ Beckmann, C. F., DeLuca, M., Devlin, J. T. & Smith, S. M. (2005). Investigations into resting-state connectivity using independentcomponent analysis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 1001–1013
  15. ↑ Bellec, P. et al. (2012). The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine forscientific workflows. Front. Neuroinform 6, 7
  16. ↑ Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., ... & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in neuroinformatics, 8
  17. ↑ Song, X. W. et al. (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS ONE 6,e25031
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