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.

翻译：我们提出了一种无需最小预处理、无偏且鲁棒的神经影像学多模态配准流程。典型的多模态研究需使用多个独立处理流程，并涉及多种选项和超参数，而本工作提出单一结构化框架，可联合处理不同影像模态。该流程采用基于学习的最新推断技术，实现快速推断，适用于每阶段包含多种模态的大规模/多队列数据集。当前流程可处理结构MRI、静息态fMRI及淀粉样蛋白PET影像。通过病例对照研究验证了衍生生物标志物的预测能力，并探究了不同影像模态间的跨模态关联。代码见：https：//github.com/acasamitjana/JUMP。