Atlas-based Imaging Data Analysis Pipeline (AIDA) for structural and functional MRI of the mouse brain
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Automated Preprocessing
It performs tasks like image re-orientation, bias-field correction, and brain extraction with minimal user input required. -
Atlas-Based Registration
AIDAmri uses the Allen Mouse Brain Reference Atlas for accurate region-based analysis of MRI data, allowing researchers to compare results across different studies efficiently. A modified atlas version with larger labels to better match MRI resolution is provided. Users can define specific regions of interest (ROIs) for analysis, such as stroke lesions. -
Modular Design
The pipeline is developed in Python, making it cross-platform and open-source, allowing for easy integration and modification. -
Validation
The pipeline was validated with different MRI datasets, including those involving stroke models, demonstrating its robustness even in the presence of significant brain deformations. -
Functional and Structural Connectivity Analysis
The output of the pipeline includes connectivity matrices that can be used for further analysis of brain network changes in health and disease.
Pipeline overview from Pallast et al.
Information latest Version 2.0
Information about Version 1.2 (Docker stable release)
Information about Version 1.1.1 (Docker pre-release)
Information about Version 1.1 (Stable)
Information about Version 1.0
We fully moved to the containerized version of AIDAmri via Docker. All information can be found in the manual above. Please report issues and bugs directly in the issue section of this repository or at gitter (Link below in the contact section).
Download here (you probably have to clone the dataset from the gin repo. The files are annexed files, also use the raw_data folder as the test data).
Mouse MRI data, acquired with Bruker 9.4T - cryo coil setup: adult C57BL7/6 mouse,
T2-weighted (anatomical scan),
DTI (structural connectivity scan),
rs-fMRI (functional connectivity scan).
Matlab script to generate a custom version of the Allen Mouse Brain Atlas.
If you encounter problems, report directly in
or
join our Open Office Hour - each Thursday 3:00 pm (UTC+2)
For all other inquiries: Markus Aswendt (markus.aswendt@uk-koeln.de)
GNU General Public License v3.0If you use our software or modify parts of it and use it in other ways, please cite:
Pallast N, Diedenhofen M, Blaschke S, Wieters F, Wiedermann D, Hoehn M, Fink GR, Aswendt M. Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri). Front Neuroinform. 2019 Jun 4;13:42.doi: 10.3389/fninf.2019.00042.
REFERENCES
- Brain Connectivity Toolbox
- Allen Mouse Brain Reference Atlas
- 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