OASIS-3 is a retrospective compilation of data for 1378 participants that were collected across several ongoing projects through the WUSTL Knight ADRC over the course of 30years. Participants include 755 cognitively normal adults and 622 individuals at various stages of cognitive decline ranging in age from 42-95yrs. All participants were assigned a new random identifier and all dates were removed and normalized to reflect days from entry into study. The dataset contains 2842 MR sessions which include T1w, T2w, FLAIR, ASL, SWI, time of flight, resting-state BOLD, and DTI sequences. Many of the MR sessions are accompanied by volumetric segmentation files produced through FreeSurfer processing. PET imaging from different tracers, PIB, AV45, and FDG, totaling over 2157 raw imaging scans and the accompanying post-processed files from the Pet Unified Pipeline (PUP) are also available in OASIS-3.

Curated T1w MRI and CT scans from diverse sources, including the CERMEP-iDB-MRXFDG, OASIS3, NeuroMorphometrics1, IXI 2, and an in-house dataset.

OASIS-3 and OASIS-4 are the latest releases in the Open Access Series of Imaging Studies (OASIS) that is aimed at making neuroimaging datasets freely available to the scientific community. By compiling and freely distributing this multimodal dataset generated by the Knight ADRC and its affiliated studies, we hope to facilitate future discoveries in basic and clinical neuroscience. OASIS-4 contains MR, clinical, cognitive, and biomarker data for 663 individuals aged 21 to 94 that presented with memory complaints. This is a unique dataset and not an update to the OASIS-3 Longitudinal Multimodal Neuroimaging dataset.

This tar file contains all the input files needed to run the coupled model CROCO-WRF with OASIS3-MCT coupler with one 2-way nested domain in CROCO. This example configuration is based on the BENGUELA region at low resolution (this is the example of regional configuration of the CROCO model: see CROCO documentationa nd tutorials for more details)

In addition to OASIS-3 data, we introduce ‘OASIS-3_AV1451’ a cross-sectional TAU PET dataset for 451 subjects including 451 PET sessions and post-processed PUP. This set is a subset of OASIS-3 subjects that have also undergone TAU (AV1451) PET imaging.

We propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18–90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age “gaps.” To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.

This set is a subset of OASIS-3 subjects that have also undergone longitudinal TAU (AV1451) PET imaging.