Diffusion-Simulated Connectivity Challenge

The methodological development in the mapping of the brain structural connectome from diffusion magnetic resonance imaging (dMRI) has raised many hopes in the neuroscientific community. Indeed, the knowledge of the connections between different brain regions is fundamental for studying brain anatomy and its function (for instance via electroencephalography or functional MRI). The reliability of the structural connectome is therefore of paramount importance. In the search for accuracy, researchers have given particular attention to linking the white matter tractography methods used for generating the connectome with information about the microstructure of the nervous tissue. This “quantitative connectome” has shown promising results (e.g. Daducci et al., 2014, Smith et al., 2015). However, the lack of realistic numerical phantoms hindered the development and validation of methods in this framework. While previous phantoms were dedicated to either validate tractography or microstructure, a better assessment of the reliability of the connectome estimation on the one hand and its adherence to the actual microstructure of the nervous tissue, on the other hand, is necessary.

The main goal of this challenge is to evaluate the performance of quantitative connectivity methods. For this scope, we have designed three datasets of simulated diffusion-weighted images from three numerical phantoms and an evaluation framework for quantitative tractography. The phantoms are composed of a large collection of synthetic tubular fibers with diameters ranging from 1.4um to 4.2um (approximately 13,000 fibers), connecting distant Regions of Interest (ROIs). The simulation substrates have a micrometric resolution and an unprecedented size of 1 cubic millimeter to mimic an image acquisition matrix of 40x40x40 voxels. Within each voxel, the signal is simulated using Monte-Carlo simulations of spins dynamics using Monte Carlo sampling with a density of one sample per micrometer cube (1,000,000,000 samples total). This is the first time this technique was used to create
phantoms of such size and complexity. After the Monte-Carlo simulation of the signal, the image is upscaled by a factor of 100, resulting in a final image size of 10cm and a voxel size of 2.5mm isotropic, compatible with conventional diffusion tractography methods. The simulated images capture the microscopic properties of the tissue (e.g. fiber diameter, water diffusing within and around fibers, free water compartment), while also having desirable macroscopic properties resembling the anatomy, such as the smoothness of the fiber trajectories. Each phantom has 16 ROIs, forming 120 possible connections (distinct pairs of ROIs). The participants will be provided with a label map of the ROIs defining the connectivity endpoints and will have to submit a weighted connectivity matrix. The estimated connection strength between any two ROIs will be compared to the ground truth total cross-sectional area of the axon-like structures connecting both ROIs. Participants will have access to one phantom for training and one phantom for validation. Participants will have to submit their estimated connectivity on a third testing phantom.

For all three phantoms, participants will have access to the noisy diffusion signal for various diffusion acquisition parameters, a mask of the white matter volume, and a label map of the endpoint connectivity. The diffusion protocol includes 360 diffusion-weighted images and 4 non-diffusion-weighted images (b=0s/mm2). The diffusion-weighted measurements are distributed over 4 b-shells (b=1000, 1925, 3094, 13191 s/mm2). Those correspond to the 3 b-shells of the ActiveAx protocol (Alexander et al., 2010) with an additional shell at b=1000s/mm2 (echo time of 0.0535s). Each shell is sampled using 90 uniformly distributed gradient directions on the sphere. For the training phantom, participants will have access to the map of intra-/extra-fiber signal fractions, the noise-less diffusion signal, the ground truth connectivity matrix, the distribution of simulated fiber diameters and trajectories, and the evaluation script to train their connectivity estimation method. Participants will also have access to a validation dataset with its corresponding connectivity matrix to evaluate the performance of their method.

Ideally, quantitative connectivity estimation for pairs of ROIs with few interconnecting synthetic fibers should be weaker than for pairs of areas with several interconnecting synthetic fibers. An evaluation and ranking of the submission in this sense could prompt the improvement of tractography methods for quantitative structural connectivity estimation in clinical or neuroscientific settings, where we expect to find connectivity estimates directly related to the number of interconnecting axons. Accordingly, participants have to prepare their submission using relevant connectivity pipelines and refrain from developing new methods tailored to the datasets of the challenge. Overall, the challenge will provide unique datasets and analysis to foster the development of tractography and connectivity estimation methods using the rich information available from the complex microstructural organization of axons.

References

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