Anatomy-Driven Brain Connectivity Mapping
Lead Research Organisation:
University College London
Department Name: Computer Science
Abstract
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Organisations
People |
ORCID iD |
| Daniel Alexander (Principal Investigator) | |
| Hui Zhang (Co-Investigator) |
Publications
Grussu F
(2016)
A framework for optimal whole-sample histological quantification of neurite orientation dispersion in the human spinal cord.
in Journal of neuroscience methods
Palombo M
(2019)
A generative model of realistic brain cells with application to numerical simulation of the diffusion-weighted MR signal.
in NeuroImage
Graham M
(2018)
A supervised learning approach for diffusion MRI quality control with minimal training data
in NeuroImage
Slattery CF
(2017)
ApoE influences regional white-matter axonal density loss in Alzheimer's disease.
in Neurobiology of aging
Tariq M
(2016)
Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI.
in NeuroImage
GHOSH A
(2016)
Computational Diffusion MRI
Shemesh N
(2016)
Conventions and nomenclature for double diffusion encoding NMR and MRI.
in Magnetic resonance in medicine
Parker TD
(2018)
Cortical microstructure in young onset Alzheimer's disease using neurite orientation dispersion and density imaging.
in Human brain mapping
| Description | Two key findings so far: - Using non-symmetric fibre orientation patterns improves brain connectivity mapping via tractography. Within the grant we have developed a method for estimating non-symmetric fibre configurations from MRI data using neighbourhood information and shown that embedding these new objects within existing tractography algorithms resolves long-standing ambiguities in brain connectivity mapping. - Dispersion is often mistaken for crossing in diffusion MRI. Various studies suggest that fibre crossing configurations arise in a large proportion of MRI voxels in white matter of the brain. Within this grant we have challenges that view noting that more continuous patterns of fibre dispersion can appear as crossing although in fact should be considered separately. Experimental work reveals that many of the voxels previously identified as containing crossings in fact are more likely to contain dispersed fibre configurations. This influences the design of future tractography algorithms for brain connectivity mapping. |
| Exploitation Route | These findings prompt further development of tractography algorithms and the underlying models they use, both of which are evolving fast. |
| Sectors | Healthcare,Pharmaceuticals and Medical Biotechnology |
| Description | EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health) |
| Amount | £6,034,274 (GBP) |
| Funding ID | EP/S021930/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 03/2028 |
| Description | Enabling clinical decisions from low-power MRI in developing nations through image quality transfer |
| Amount | £1,020,000 (GBP) |
| Funding ID | EP/R014019/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2018 |
| End | 01/2021 |
| Description | Image Quality Transfer |
| Amount | £60,000 (GBP) |
| Organisation | Microsoft Research |
| Sector | Private |
| Country | Global |
| Start | 10/2015 |
| End | 09/2018 |
| Description | Learning MRI and histology image mappings for cancer diagnosis and prognosis |
| Amount | £774,000 (GBP) |
| Funding ID | EP/R006032/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 12/2017 |
| End | 01/2020 |