Periodic Reporting for period 1 - MWMI (Mesoscopic characterization of human white-matter: a computational in-vivo MRI framework)
Reporting period: 2015-12-01 to 2017-11-30
The goal of this proposal was to develop a novel computational in-vivo MRI method, which we call Mesoscopic White-Matter magnetic resonance Imaging (MWMI). MWMI will allow the assessment of 4 specific micro-scale metrics at high spatial resolution: myelin, water concentration, axonal density and the ratio between inner and outer fiber diameters (g-ratio) - a surrogate measure for the conductance speed in a fiber. Conventional quantitative MRI (qMRI), such as Diffusion Tensor Imaging (DTI), can detect microstructural changes but do not provide any information on the origin of these changes. In contrast, MWMI is capable of detecting microstructural changes and in addition provides insight into the underlying processes leading to these changes (e.g. whether pain leads to axonal reorganization or (de)myelination).
To main objectives of this project can be summarized as follows:
(i) To develop biophysical models that link the MR signal to the microscopic tissue composition.
(ii) To achieve high spatial resolution and integration of different MRI techniques.
(iv) To apply these MWMI methods to the pain circuit, which is a fundamental and well-described circuit of the human CNS.
(v) To disseminate the MWMI method to the community by providing an imaging protocol and an open-source toolbox to the community.
1. We developed biophysical models that relate different quantitative MRI techniques to the underlying tissue composition.
2. We developed image processing methods that greatly enhance the quality of quantitative MRI maps thereby facilitating the integration of different qMRI techniques, suffering from different imaging artifacts.
3. We estimated high-resolution maps of biophysical markers (e.g. myelin or fiber densities) from quantitative MRI maps by combining an efficient imaging protocol with the developed, novel and efficient biophysical models (1.) and new processing methods (2.).
4. We showed the feasibility of using the MWMI method in a neuroscience study, where we acquired behavioral and quantitative MRI data with the aim of revealing that nociceptive long-term habituation affects microstructure.
5. We developed open-source toolboxes for: (a) processing and estimating quantitative parameter maps, (b) adaptive denoising of quantitative MRI maps, and (c) processing DTI maps in the spinal cord.
• The biophysical NODDI-DTI method increases the understanding of how physical DTI metrics, which are routinely used in clinical and neuroscience research, are related to biophysical properties such as the density of axons in white matter (see Figure).
• Our newly developed method for artifact correction in spinal cord DTI reduces the artifact-induced variability across subjects and thus improves the statistical power for detecting group differences. In principle, this method can be extended to other model-based qMRI techniques, such as the MPM model.
These main achievements of these Marie-Curie project are the main pillars of MWMI and built the foundation for neuroscience studies using multi-modal qMRI.
In terms of communication and results dissemination, these happened across different channels:
• The project website (https://goo.gl/zsVcgP) is part of our institutes' website, which includes news, articles and code releases.
• Tweets posts by the author (@siawooshm) and journals (e.g. @FrontNeurosci).
• Presentations at 5 international conferences, including two oral presentations: one at the SFN 2016, San Diego and at the ISMRM 2017 in Hawaii.
• Invited speaker on computational MRI, biophysical models, and MRI-based in vivo histology. 39th Annual Meeting of the Japan Neuroscience Society 2016, Yokohama, Japan.
• At the ESMRMB lecture on “Quantitative MRI for Characterising Brain Tissue Microstructure” in Leipzig, 2016, the author has given a presentation about biophysical models using diffusion MRI.
• The methods developed in this Marie-Curie project are (or will become) part of two open-source toolboxes: (a) the method for estimating a myelin biomarker (which can be calculated from the proton density maps) is part of the hMRI toolbox and available here: b the methods for artifact correction in spinal cord DTI will become available under www.diffusiontools.com after the paper has been published.