  We present a pipeline for unbiased and robust multimodal registration of
neuroimaging modalities with minimal pre-processing. While typical multimodal
studies need to use multiple independent processing pipelines, with diverse
options and hyperparameters, we propose a single and structured framework to
jointly process different image modalities. The use of state-of-the-art
learning-based techniques enables fast inferences, which makes the presented
method suitable for large-scale and/or multi-cohort datasets with a diverse
number of modalities per session. The pipeline currently works with structural
MRI, resting state fMRI and amyloid PET images. We show the predictive power of
the derived biomarkers using in a case-control study and study the cross-modal
relationship between different image modalities. The code can be found in
https: //github.com/acasamitjana/JUMP.
