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Workflows for automated analysis of mouse brain imaging data
Aref Kalantari, Leon Scharwächter, Felix Schmitt, Niklas Pallast, Gereon R. Fink, Markus Aswendt
Presenting author:
Markus Aswendt
An efficient way to integrate imaging data sets with variable image resolution and image content is key for cross-modality correlations, e.g., structural to functional or in vivo biomarker to ex vivo histology. Here, we will present our atlas-based approach in which all contributing images are accurately aligned through image registration algorithms with the Allen Mouse Brain Atlas (CCF v3). The atlas resource facilitates the analysis of macroscopic to microscopic features across subjects and integration of viral tracing and gene expression data. With the atlas-based imaging data analysis (AIDA) tools, we have created automated workflows for the specific needs of cross-modality mouse brain experiments, including AIDAmri (conversion and postprocessing of T2w, fMRI, and DTI), AIDAconnect (graph theoretical algorithms for structural and functional network analysis), and AIDAhisto (cell counting on immunohistochemical brain sections). The workflows (available on were developed in Python or Matlab building on existing packages and algorithms, e.g. Nipype, NiftyReg, FSL, and the Brain Connectivity Toolbox. We will discuss recent AIDA applications and highlight the advantages of atlas-based image analysis to characterize brain-wide network to cellular changes after brain lesions.
Acknowledgements: This work was financial support by the Friebe Foundation (T0498/28960/16) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 431549029 – SFB 1451.