Figure 1.

Schematic overview of AIDAmri processing modules and subsequent computational steps for anatomical data (T2-weighted and T2 map), structural (diffusion tensor imaging, DTI) and functional data (resting-state functional magnetic resonance imaging, rs-fMRI). The given image function I(x) represents the 3D MRI image space and describes all intensities at the position $x=\{\overrightarrow{x},\overrightarrow{y},\overrightarrow{z}\}$. All data types are pre-processed using a re-orientation f_re(x), bias correction f_bc(x) and brain extraction f_ex(x). The user has the opportunity to define individual regions of interest (ROIs), e.g., a lesion mask, to compare particular areas over different measurements by generating an incidence map. The combined transformation f of the affine f_A and non-linear transformation f_NL is applied to MRI template MTPL I_T(x) and subsequently the ARA I_A(x) with the pre-processed data set I^T2(x). DTI I^DTI(x) and rs-fMRI I^fMRI(x) processing steps were implemented based on established protocols (Budde and Song, 2010; Kim et al., 2012; Gorges et al., 2017). AIDAmri generates a variety of outputs such as the connectivity matrices which can be used for further atlas-based connectivity analysis. Icons designed by Smashicons from www.flaticon.com.