AIDAconnect

Atlas-based Imaging Data Analysis Pipeline (AIDA) for structural and functional MRI of the mouse brain

AIDAconnect is a comprehensive collection of MATLAB scripts for mouse brain MRI data analysis and connectivity analysis using graph theory. A detailed guide for the installation process and the usage of AIDAconnect is written in the manual.

Information about Version 1.0
Information about Version 1.1

INSTALLATION

=> MANUAL

=> TEST DATA

The given tools from AIDAconnect are provided for anatomical T2-weighted MRI (T2w), diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) data and are currently designed for creating weighted-undirected graphs only.

CONTACT

Leon Scharwächter and Markus Aswendt (contact: [email protected])

Need help? Chat with us and find answers to FAQs in the AIDA_Tools Gitter rooms:

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LICENSE GNU General Public License v3.0

REFERENCES

• AIDAmri
  • Pallast, N., et al. "Processing pipeline for Atlas-based Imaging Data Analysis (AIDA) of structural and functional mouse brain MRI" Frontiers in Neuroinformatics, 2019
• Brain Connectivity Toolbox
  • M. Rubinov and O. Sporns (2010). Complex Network Measures of Brain Connectivity: Uses and Interpretations. NeuroImage 52 (3), 1059–69.
• Allen Mouse Brain Reference Atlas
  • Wang et al. (2020). The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell 181 (4), 936-953.
• Niftyreg
  • Ourselin, et al. (2001). Reconstructing a 3D structure from serial histological sections. Image and Vision Computing, 19(1-2), 25–31.
  • Modat, et al. (2014). Global image registration using a symmetric block- matching approach. Journal of Medical Imaging, 1(2), 024003–024003.
  • Rueckert, et al. (1999). Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging, 18(8), 712–721.
  • Modat, et al. (2010). Fast free-form deformation using graphics processing units. Computer Methods And Programs In Biomedicine,98(3), 278–284.
• FSL
  • M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith. Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45:S173-86, 2009
  • S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-19, 2004
  • M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012
• DSIstudio
  • Yeh, Fang-Cheng, et al. Deterministic diffusion fiber tracking improved by quantitative anisotropy. (2013): e80713. PLoS ONE 8(11)