Anatomy-Driven Brain Connectivity Mapping
Lead Research Organisation:
University College London
Department Name: Computer Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
People |
ORCID iD |
Daniel Alexander (Principal Investigator) | |
Hui Zhang (Co-Investigator) |
Publications
Slattery C
(2015)
IC-P-134: Neurite orientation dispersion and density imaging (NODDI) in young-onset Alzheimer's disease and its syndromic variants
in Alzheimer's & Dementia
Grussu F
(2017)
Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?
in Annals of clinical and translational neurology
Zhang J
(2018)
In vivo characterization of white matter pathology in premanifest huntington's disease.
in Annals of neurology
Sepehrband F
(2016)
Parametric Probability Distribution Functions for Axon Diameters of Corpus Callosum.
in Frontiers in neuroanatomy
Parker TD
(2018)
Cortical microstructure in young onset Alzheimer's disease using neurite orientation dispersion and density imaging.
in Human brain mapping
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
Slator PJ
(2018)
Placenta microstructure and microcirculation imaging with diffusion MRI.
in Magnetic resonance in medicine
Drobnjak I
(2016)
PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study.
in Magnetic resonance in medicine
Shemesh N
(2016)
Conventions and nomenclature for double diffusion encoding NMR and MRI.
in Magnetic resonance in medicine
Slattery CF
(2017)
ApoE influences regional white-matter axonal density loss in Alzheimer's disease.
in Neurobiology of aging
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 |