Advanced developments of diffusion MRI tractography computational methods

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The structural connectome is at the heart of modern human brain mapping and neuroinformatics. Neurodegenerative diseases such as Alzheimer's disease, developmental disorders such as autism, brain tumors and traumatic brain injuries such as concussions affect a large proportion of the Canadian population. It remains unknown how fiber connections in the white matter are altered, degenerated and damaged by these disorders. Diffusion magnetic resonance imaging (MRI) is the only non-invasive technique able to image the neural architecture of the white matter and better understand how the brain is wired. Diffusion MRI is thus a tool to measure the connectome at the macroscale, i.e. for short and long distances connections at the millimeter scale.  A crucial part of mapping the white matter connectome are measurements computed from dMRI tractography. Tractography is the computerized process of reconstructing white matter fiber bundles. Unfortunately, current tractography techniques do not control for invalid connections produced by algorithms, do not reconstruct the full extent of existing tracts, and is not a quantitative technique. As of today, the lack of accuracy in published works using tractography is well known but has mostly been overlooked because there are no public databases to validate techniques. There is mounting evidence that tractography errors can lead to wrong connectivity interpretations.  The main objective of my research program is to propose a diffusion MRI "tractography revolution". Tractography must be solved as a difficult ill-posed inverse problem, which needs all the external information possible to hope to solve the problem with high quality, accuracy and reproducibility. A first short-term objective is to propose a standardized & open database for tractography development evaluation of success. A second short-term objective is to incorporate advanced multi-dimensional (MD) mathematics and physics into the tractography process. A mid-term objective is to propose a computational framework that incorporates anatomical knowledge, microstructure information and multi-modality information inside the tracking process. Another mid to long-term objective is to develop machine learning techniques that learn from mistakes, learn what to track and where not to track, by incorporating the past information of successes and failures.    At the era of connectomics applications, it is urgent for neuroscience and medicine to address the fundamental limitations of tractography and structural brain mapping. The potential value of having new and quantitative tractography tools is extremely high. This will have a significant impact on neurosciences in Canada and worldwide because it is central to human brain mapping and connectome projects. It will help better characterize the widespread, large-scale neuronal networks that underlie several of the most complex human neurological disorders.