Biology subjects

Descoteaux, M.

Publications and source records attributed to Descoteaux, M..

5 recordsLinked to original sources

bioRxiv2018

Bundle-specific fornix reconstruction for dual-tracer PET-tractometry

Tractography is known to have problems reconstructing white matter bundles that are narrow, have high curvature, or go through partial volume voxels contaminated by CSF or gray matter. One such bundle is the fornix, the major output tract of the hippocampus, which is especially problematic with aging. Hippocampal atrophy and ventricular expansion make the fornix even harder (often impossible) to track with current state-of-the-art techniques. In this work, a bundle-specific tractography algorithm is proposed to fully reconstruct the fornix. By injecting shape, position, and orientation priors, fornix reconstruction is markedly is improved. We report an increase in spatial coverage and better reproducibility across test-retest. These improvements over classical tractography algorithms also enable tractometry of the fornix to be combined with dual-tracer positron emission tomography (PET) data in participants with mild cognitive impairment (MCI). MCI participants underwent a multi-modal brain imaging before and after a 6-month daily ketogenic supplement. We report, for the first time, significant diffusion measures and 18F-fluorodeoxyglucose (FDG) uptake differences in specific sub-sections of the fornix after the ketogenic supplement.

neuroscience↗

bioRxiv2018

Limits to anatomical accuracy of diffusion tractography using modern approaches

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the ISBI 2018 3D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractographys inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

neuroscience↗

bioRxiv2018

A probabilistic atlas of the human pyramidal tract in 410 healthy participants

With the advances in diffusion MRI and tractography, numerous atlases of the human pyramidal tract (PyT) have been proposed but the inherent limitation of tractography to resolve crossing bundles within the centrum semiovale have so far prevented the complete description of the most lateral PyT projections. Here, we combined a precise manual positioning of individual subcortical regions of interest along the descending pathway of the PyT with a new bundle-specific tractography algorithm. This later is based on anatomical priors to improve streamlines tracking in crossing areas. We then extracted both left and right PyT in a large cohort of 410 healthy participants and built a population-based atlas of the whole-fanning PyT with a complete description of its most cortico-lateral projections. Clinical applications are envisaged, the whole-fanning PyT atlas being likely a better marker of corticospinal integrity metrics than those currently used within the frame of prediction of post-stroke motor recovery. The present population-based PyT, freely available, provides an interesting tool for clinical applications in order to locate specific PyT damage and its impact to the short and long-term motor recovery after stroke.

neuroscience↗

bioRxiv2017

Learn to Track: Deep Learning for Tractography

We show that deep learning techniques can be applied successfully to fiber tractography. Specifically, we use feed-forward and recurrent neural networks to learn the generation process of streamlines directly from diffusion-weighted imaging (DWI) data. Furthermore, we empirically study the behavior of the proposed models on a realistic white matter phantom with known ground truth. We show that their performance is competitive to that of commonly used techniques, even when the models are used on DWI data unseen at training time. We also show that our models are able to recover high spatial coverage of the ground truth white matter pathways while better controlling the number of false connections. In fact, our experiments suggest that exploiting past information within a streamline's trajectory during tracking helps predict the following direction.

neuroscience↗

bioRxiv2017

Fiber tractography using machine learning

We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.

neuroscience↗