MRA: A multimodal MRI-derived amyloid- biomarker for Alzheimer's disease

Florbetapir F-18 (AV45), a highly sensitive and specific positron emission tomographic (PET) molecular biomarker binding to the amyloid-beta of Alzheimer's disease (AD), is constrained by radiation and cost. We sought to combat it by combining multimodal magnetic resonance imaging (MRI) images and a collaborative generative adversarial networks model (CollaGAN) to develop a multimodal MRI-derived Amyloid-beta (MRA beta) biomarker. We collected multimodal MRI and PET AV45 data of 380 qualified participants from the ADNI dataset and 64 subjects from OASIS3 dataset. A five-fold cross-validation CollaGAN were applied to generate MRA beta. In the ADNI dataset, we found MRA beta could characterize the subject-level AV45 spatial variations in both AD and mild cognitive impairment (MCI). Voxel-wise two-sample t-tests demonstrated amyloid-beta depositions identified by MRA beta in AD and MCI were significantly higher than healthy controls (HCs) in widespread cortices (p <.05, corrected) and were much similar to those by AV45 (r >.92, p <.001). Moreover, a 3D ResNet classifier demonstrated that MRA beta was comparable to AV45 in discriminating AD from HC in both the ADNI and OASIS3 datasets, and in discriminate MCI from HC in ADNI. Finally, we found MRA beta could mimic cortical hyper-AV45 in HCs who later converted to MCI (r =.79, p <.001) and was comparable to AV45 in discriminating them from stable HC (p >.05). In summary, our work illustrates that MRA beta synthesized by multimodal MRI could mimic the cerebral amyloid-beta depositions like AV45 and lends credence to the feasibility of advancing MRI toward molecular-explainable biomarkers.