The basic requirements in neuroimaging research are changing rapidly due to the growing size of datasets and multimodal approaches with increasingly complex, time-consuming, diverse and fast-moving standards. Neuromodulation And Multimodal NeuroImaging software (NAMNIs) offers a ready-to-use, open-source pipeline for pre- and post-processing of multimodal neuroimaging and neuromodulation data. A strong focus is maintained on reproducibility and support for multi-platform parallelization. The calculations are performed in native volume and surface spaces as well as in MNI standard space. Input and output data of these calculations conform the international Brain Imaging Data Structure (BIDS) format. The software should enable the user to better interpret results using MRI-based modalities and to calculate simulations based on these data, which can later be compared with the results of neuromodulation pilot or clinical studies. The simple integration into a high-performance computing (HPC) environment allows the calculation of large amounts of data or retrospectively combined samples in a feasible period of time. NAMNIs consists of a processing pipeline for multimodal magnetic resonance imaging (MRI) data analysis using parallel processing. It performs various pre-processing steps with the aforementioned data, calculating relevant metrics such as the number of activated voxels (spatial extent), within regions of interest (ROIs) effects, ROI-to-whole-brain calculations, probabilistic values, motion parameters, connectivity strength values (standardized in z scores), and more. This is implemented in native space, standard MNI space, and surface space for structural T1-, T2-weighted data. In addition, structural T1- and T2-data are used for the simulation of non-invasive brain stimulation. Calculation steps are performed in parallel with different topologies to allow processing of large datasets in reasonable time. It is applicable to HPC and is provided as research software that is free and open-source software (FOSS). Already in the proof-of-concept and prototype phase, the project was published as abstracts of international conferences (Karali et al., 2017) (Karali et al., 2019).