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| Home > Publications database > Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri) > print |
| 001 | 863613 | ||
| 005 | 20210130002231.0 | ||
| 024 | 7 | _ | |a 10.3389/fninf.2019.00042 |2 doi |
| 024 | 7 | _ | |a 2128/22468 |2 Handle |
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| 037 | _ | _ | |a FZJ-2019-03625 |
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| 100 | 1 | _ | |a Pallast, Niklas |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri) |
| 260 | _ | _ | |a Lausanne |c 2019 |b Frontiers Research Foundation |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies. |
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| 700 | 1 | _ | |a Diedenhofen, Michael |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Blaschke, Stefan |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Wieters, Frederique |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Wiedermann, Dirk |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Hoehn, Mathias |0 P:(DE-Juel1)176651 |b 5 |u fzj |
| 700 | 1 | _ | |a Fink, Gereon R. |0 P:(DE-Juel1)131720 |b 6 |u fzj |
| 700 | 1 | _ | |a Aswendt, Markus |0 P:(DE-HGF)0 |b 7 |e Corresponding author |
| 773 | _ | _ | |a 10.3389/fninf.2019.00042 |g Vol. 13, p. 42 |0 PERI:(DE-600)2452979-5 |p 42 |t Frontiers in neuroinformatics |v 13 |y 2019 |x 1662-5196 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/863613/files/fninf-13-00042.pdf |
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