The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

Abstract

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

Fig. 1

Data quality and preprocessing. (a) Axial view of dMRI (left, non-diffusion weighted volume, B0), aligned anatomical image (center) and white matter mask obtained from the anatomy (white), overlaid on the B0 to show the quality of the white matter volume delineation. One example subject is reproduced from the Stanford (top), Human Connectome 3T (middle) and Human Connectome 7T (bottom) data. (b) Mean and ±1 sd across diffusion-weighted measurements of the signal-to-noise (SNR) for each subject and dataset in the O3D distribution as implemented at.

Fig. 2

Estimated fiber orientation distribution functions (fODF). (a) Examples of estimated single-fiber response function used to compute the fODF individually in each subject. The similarity and flat shape of the response functions ensures model-fit quality^,. (b) Axial brain views from three example subjects in each dataset depicting the estimated fODF (fiber orientation distribution functions) in a series of voxels covering the corpus callosum and the central-semiovale. Coverage of the response functions and orientation are consistent with major anatomical understanding.

Fig. 3

Visualization of whole-brain tractograms and fascicle length distribution. (a) The full brain tractography for each of the three datasets, as generated using DTI_{deterministic}, CSD_{deterministic} and CSD_{probabilistic} Models. (b) The whole-brain connectome streamline count for each of the three tractography models applied to the STN, HCP3T and HCP7T datasets.

Fig. 4

Anatomy of tracts and number of fascicles per tract. (a) The morphologies of several major tracts, overlaid with one another, as segmented from whole brain connectomes. Tractography generated for each dataset using DTI_{deterministic}, CSD_{deterministic} and CSD_{probabilistic} models. Colors correspond to individual tracts. (b) The streamline counts associated with several major tracts. Marker color corresponds to tractography model. Error bars generated from standard deviation across ten replications.

Fig. 5

Brain network matrices. Nine representative matrices of connectivity between anatomical regions defined in the Desikan-Killiany atlas. Matrices report fiber density computed as twice the number of streamlines touching a pair of regions divided by the combined size of the two regions (in number of brain voxels). Density is normalized across matrices, brighter colors indicate higher density. Networks depicted were generated for three representative subjects, one per dataset, using DTI_{deterministic}, CSD_{deterministic} and CSD_{probabilistic} tractography.