TY - JOUR
T1 - Non-negative data-driven mapping of structural connections with application to the neonatal brain
AU - Thompson, E.
AU - Mohammadi-Nejad, A. R.
AU - Robinson, E. C.
AU - Andersson, J. L.R.
AU - Jbabdi, S.
AU - Glasser, M. F.
AU - Bastiani, M.
AU - Sotiropoulos, S. N.
N1 - Funding Information:
E.T. is supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [ONBI CDT, grant number EP/L016052/1]. S.N.S. is also supported by grant [217266/Z/19/Z] from the Wellcome Trust. A.R.M is supported by the NIHR Nottingham Biomedical Research Centre [BRC-1215-20003]. Data were provided by the developing Human Connectome Project, a KCL-Imperial-Oxford Consortium funded by the European Research Council under the European Union Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. [319456]. We are grateful to the families who generously supported this trial. The computations described in this paper were performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster, which provide High Performance Computing service to the University's research community.
Funding Information:
E.T. is supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [ONBI CDT, grant number EP/L016052/1]. S.N.S. is also supported by grant [217266/Z/19/Z] from the Wellcome Trust. A.R.M is supported by the NIHR Nottingham Biomedical Research Centre [BRC-1215-20003]. Data were provided by the developing Human Connectome Project, a KCL-Imperial-Oxford Consortium funded by the European Research Council under the European Union Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. [319456]. We are grateful to the families who generously supported this trial. The computations described in this paper were performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster, which provide High Performance Computing service to the University's research community.
Publisher Copyright:
© 2020
PY - 2020/11/15
Y1 - 2020/11/15
N2 - Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.
AB - Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.
UR - http://www.scopus.com/inward/record.url?scp=85089918540&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2020.117273
DO - 10.1016/j.neuroimage.2020.117273
M3 - Article
C2 - 32818619
AN - SCOPUS:85089918540
SN - 1053-8119
VL - 222
JO - NeuroImage
JF - NeuroImage
M1 - 117273
ER -